Load imbalance in distributed key-value (KV) storage systems, especially under skewed access patterns across racks or clusters, can significantly degrade performance. We present CacheMigrate, a switch-assisted caching and migration framework that leverages programmable Top-of-Rack (ToR) switches for real-time hotspot detection, in-network caching, and per-key migration state tracking. By decoupling data movement from request handling, CacheMigrate minimizes service disruption and ensures strong consistency during live migration. A distributed migration state management design coordinates ToR and spine switches without centralized bottlenecks. We implement CacheMigrate on a P4-programmable switch and evaluate it using realistic workloads. Results show that CacheMigrate consistently delivers higher throughput and better load balance than existing approaches across diverse workload and system configurations.

Radio Access Networks (RAN) are anticipated to gradually transition towards Cloud RAN (C-RAN), leveraging the full advantages of the cloud-native computing model. While this paradigm shift offers a promising architectural evolution to improve scalability, efficiency, and performance, significant challenges remain in managing the massive computing requirements of physical layer (PHY) processing. To address these challenges and meet the stringent Service Level Objectives (SLOs) in 5G networks, hardware acceleration technologies are essential. In this paper, we aim to mitigate this challenge by offloading 5G modulation mapping, a critical yet demanding function to encode bits into IQ symbols, directly onto the switch ASICs. Specifically, we introduce NetMod, a 5G New Radio (NR) standard-compliant in-network modulation mapper accelerator. NetMod leverages the capabilities of new-generation programmable switches within the C-RAN infrastructure to offload and accelerate PHY modulation functions. We implemented a NetMod prototype on a real-world platform using the Intel Tofino programmable switch and commodity servers running the Data Plane Development Kit (DPDK). Through extensive experiments, we demonstrate that NetMod achieves modulation mapping at switch line rate using minimal switch resources, thereby preserving ample space for traditional switching tasks. Furthermore, comparisons with a GPU-based 5G modulation mapper show that NetMod is 2.2$\boldsymbol{\times}$to 3.3$\boldsymbol{\times}$faster using only a single switch port. These results highlight the potential of in-network acceleration to enhance 5G network performance and efficiency.

In 5G Radio Access Networks (RAN), Cyclic Redundancy Check (CRC) algorithms play a vital role in detecting accidental changes to digital data during transmission. However, due to the massive bandwidth demands in 5G networks, CRC computation is a resource-intensive process. To address this challenge, we propose performing CRC computation and verification directly in the network path. Specifically, we introduce NetCRC-NR, a 5G New Radio (NR) standard-compliant in-network CRC accelerator. NetCRC-NR implements the 5G NR CRC algorithms specified in 3GPP TS 38.212, including CRC24A, CRC24B, CRC24C, CRC16, CRC11, and CRC6. It leverages programmable switches to perform in-network CRC generation and validation for the Transport Blocks (TBs) and Code Blocks (CBs), aiming at providing high CRC computation throughput and alleviating the computational burden on General-Purpose Processors (GPPs). We design and implement NetCRC-NR on Intel Tofino programmable switch and commodity servers running the Data Plane Development Kit (DPDK). Extensive experiments demonstrate that NetCRC-NR performs CRC generation and verification at the switch line rate of up to 4+Tbps CRC throughput, showcasing its efficiency and potential in accelerating the 5G RAN error detection process.

Wireless Sensor Networks (WSNs) are used to sense and monitor physical conditions in various services and applications. However, there are a number of challenges in deploying WSNs, especially those pertaining to energy replenishment. Using the current solutions, when a significant number of sensors need to replenish their energy, this would be costly in terms of time, efforts and resources. Thus, this paper aims to solve this problem by efficiently deploying wireless power transfer technologies and scheduling Mobile Charging Vehicles (MCVs) in WRSN. The proposed method deploys multi-criteria decision-making (i.e., Analytical Hierarchy Process (AHP)) to schedule the charging tasks. To the best of our knowledge, this paper is the first to depend solely on AHP in MCVs scheduling. The paper demonstrates the validity of the proposed method by illustrating that the matrices that are created are within the accepted values of consistency ratio. In addition, the paper proposes a method of partitioning the values of our criteria to avoid the problem of different criteria having different measurement units. Unlike existing works, the paper aims to schedule an MCV for charging based on both the distance and residual energy of the sensor. The proposed method exhibits superiority in terms of the average remaining energy available in the system, having the shortest queue length, shorter MCV response time, shorter charging duration, and shorter queue waiting time against the state-of-the-art methods. Our study paves the way for next generation efficient charging and MCV scheduling.

Wireless Power Transfer (WPT) is a promising technology that can potentially mitigate the energy provisioning problem for sensor networks. In order to efficiently replenish energy for these battery-powered devices, designing appropriate scheduling and charging path planning algorithms is essential and challenging. Whilst previous studies have tackled this challenge, the conjoint influences of network topology, charging path planning, and energy threshold distribution in Wireless Rechargeable Sensor Networks (WRSNs) are still in their infancy. We mitigate the aforementioned problem by proposing novel algorithmic solutions to efficient sector-based on-demand charging scheduling and path planning. Specifically, we first propose a hexagonal cluster-based deployment of nodes such that finding an NP-Complete Hamiltonian path is feasible. Second, each cluster is divided into multiple sectors and a charging path planning algorithm is implemented to yield a Hamiltonian path, aimed at improving the Mobile Charging Vehicle (MCV) efficiency and charging throughput. Third, we propose an efficient algorithm to calculate theimportanceof nodes to be used for charging duration decision-making and prioritization. Fourth, a non-preemptive dynamic priority scheduling algorithm is proposed for charging tasks’ assignments and scheduling. Finally, extensive simulations have been conducted, revealing the significant advantages of our proposed algorithms in terms of energy efficiency, response time, dead nodes’ density, and queuing processing.

Smart Parking has gained so much popularity in recent years due to the increasing number of vehicles in big cities, resulting in traffic congestion in urban areas. Not only on the streets but also in places such as educational institutions, hospitals, commercial activities, special events, and entertainment uses. Finding a free parking lot in these places has evolved difficulty for the drivers. To solve such a problem, governments and researchers tried to find alternative solutions to overcome or mitigate the traffic congestion. Many solutions have been proposed, such as increasing the parking capacities, which takes much time or makes it hard to find a square area in crowded places. Most existing studies, do not consider the cost of deployment, energy, and time-to-market consideration which makes the available systems need further investigation. In this paper, we propose an intelligent parking system prototype that can be useful for the drivers to have a prior knowledge about the available parking lots in the area of interest. Our proposed system involves deploying mmWave Radar sensor nodes in each parking lot to detect the availability of parking vehicle through transmitting radio pulses periodically. The detected information can be sent to the gateway through multi-hop for further statistics and reports. We also give an intensive analysis and study about the challenges and consideration on the mmWave radar design aiming to improve the detection accuracy and avoid false-detection that occurs from objects near to the sensor. To ensure continuous operation and extend sensor life-time, we propose Bluetooth Low Energy BLE-enabled relay-feature as the communication protocol between the nodes.