Reconfigurable intelligent surfaces (RIS) can reflect an incoming signal toward a target location to enhance the received signal quality. In most studies, it is assumed that the base station (BS) provides configuration data to an RIS controller. However, for a passive RIS with a high number of elements, this data transmission from the BS can introduce significant delays. This challenge is especially pronounced in vehicular communications, where reflection coefficients must be updated even for a few millimeters of movement. To overcome this limitation, we propose a self-tuning RIS controller that independently calculates the reflection coefficients with minimal input from the BS. Our RIS controller leverages the user’s location information to compute the reflection coefficients in three steps: channel gain calculation, steering vector calculation, and optimal phase control. A pipelined architecture is developed to support these steps in real-time. The proposed VLSI architecture is implemented on an Artix-7 XC7A100TCSG324-1 FPGA to calculate reflection coefficients for a vehicle moving at a 100 kmh speed.

Abstract Fog computing is a promising technique to reduce the latency and power consumption issues of the Internet of Things (IoT) ecosystem by enabling storage and computational resource close to the end‐user devices with additional benefits such as improved execution time and processing. However, with an increase in IoT devices, the resource allocation and job scheduling became a complicated and cumbersome task due to limited and heterogeneous resources along with the locality restriction in such computing environment. Therefore, this paper proposes a cache‐based approach for efficient resource allocation in fog computing environment, while maintaining the quality of service. The proposed algorithm is realized using iFogSim simulator and a comprehensive comparison is presented with the traditional First Come First Served and Shortest Job First policies. The performance evaluation revealed that with the proposed scheme the execution time, latency, processing delays and power consumption decreased by 38%, 11.1%, 6%, and 17.8%, respectively, as compared to those of the traditional schemes.

Summary There is an abundant expansion in the race of technology, specifically in the production of data, because of the smart devices, such as mobile phones, smart cards, sensors, and Internet of Things (IoT). Smart phones and devices have undergone an enormous evolution in a way that they can be used. More and more new applications, such as face recognition, augmented reality, online interactive gaming, and natural language processing are emerging and attracting the users. Such applications are generally data intensive or compute intensive, which demands high resource and energy consumption. Mobile devices are known for the resource scarcity, having limited computational power and battery life. The tension between compute/data intensive application and resource constrained mobile devices hinders the successful adaption of emerging paradigms. In the said perspective, the objective of this article is to study the role of computation offloading in mobile cloud computing to supplement mobile platforms ability in executing complex applications. This article proposes a systematic approach (EFFORT) for offload communication in the cloud. The proposed approach provides a promising solution to partially solve energy consumption issue for communication‐intensive applications in a smartphone. The experimental study shows that our proposed approach outperforms its counterparts in terms of energy consumption and fast processing of smartphone devices. The battery consumption was reduced to 19% and the data usage was reduced to 16%.

With advances in fog and edge computing, various problems such as data processing for large Internet of Things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid (SG) IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for SGs incur high computation and communication costs, and in recent years, there have been efforts to leverage fog computing with SGs to overcome these limitations. In this article, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of the Paillier cryptosystem is used to encrypt the consumption data of the smart meter (SM) users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the SMs malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption, and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the privacy-preserving fog-enabled data aggregation scheme.

Recent advancements in cellular technologies allow discrete computing devices embedded with sensors to communicate over long distances with low-cost and low-energy consumption. This could drastically impact the future internet of thing (IoT) ecosystem. In this regard, Third Generation Partnership Project (3GPP) has introduced a new cellular-based technology called Narrowband Internet of Things (NB-IoT) which is one of the potential technologies for enabling IoT application in vehicular, health-care, industry 4.0, etc. However, NB-IoT technology is still in its infancy and so far it is unclear whether it is sufficiently reliable to complement or replace existing short-range and cellular technologies to enable such use-case scenarios. Therefore, this paper presents a preliminary investigation of the coverage of NB-IoT in three different scenarios i.e., outdoor, indoor, and underground with empirical measurements, one of the key performance indicators in which operators are most interested. The measurements were conducted on Tallinn University of Technology (TalTech) campus and nearby residential areas within a range of 700 m. The obtained results indicate that NB-IoT is able to provide good connectivity to meet the IoT application requirements in outdoor and indoor environments. However, for an underground scenario with the beam enabled for 700 m, it is only possible to provide connectivity to the devices up to 400 m. Furthermore, it is also observed that NB-IoT is able to provide connectivity to devices with a received signal strength indicator (RSSI) value as low as −105 dBm as compared to −95 dBm as in long-term evolution (LTE).