REMOS-IoT-A Relay and Mobility Scheme for Improved IoT Communication Performance

Abstract

The Internet of Things (IoT) can avail from device-to-device (D2D) communication techniques to increase object data exchange performance. IoT networks aim to offer a massive number of services at high quality levels, and many of the devices providing these services are mobile. Devices such as wearables, sensors, drones and smart vehicles need constant connectivity despite their moving patterns and therefore, an IoT architecture should consider both Quality of Service (QoS) and mobility. D2D allows devices to communicate directly to share content and functionality, such as access to the Internet. This paper proposes REMOS-IoT -A RElay and MObility Scheme for improved IoT communication performance in support of increased QoS for the data exchange services between mobile IoT devices. Simulation-based testing showed how performance of devices increased in several scenarios, demonstrating the efficiency of the proposed architecture and algorithms.

Average-Case Analysis of Greedy Matching for Large-Scale D2D Resource Sharing

“…In each round, we depict the network graph as G = (U, E), where U is the set of nodes corresponding to users that are participating in the given round, and E is the set of D2D links between participating users that are feasible to establish with some minimum level of performance (e.g., signal strength, actual physical distance, etc., depending on the application). Our model reasonably assumes a time-scale separation between the time for users to change location and the time to perform a round of the algorithm (that typically should be in the order of a few seconds), in order to establish stable D2D communication for resource sharing [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. This might not hold in the case of fast-moving cars but it is the case when users typically hang around in crowded urban areas with low mobility, e.g., walking streets, airports, stadiums, city parks, cafes, malls, etc.. For each user u i ∈ U = {u 1 , u 2 , .…”

“…In particular, when q i = 1 for all user u i , ( 14) degenerates to (6). Note that in (14) we need to take expectation over both weight W and quantity Q distributions to compute the average performance.…”

“…Given the large diversity for each user in the levels of her individual resource utilization, device-todevice (D2D) resource sharing is envisioned as a promising approach to pool resources and increase social welfare. Some recent studies have been conducted for modeling and guiding D2D resource sharing in wireless networks (e.g., [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]). As a node in the established D2D network graph, each mobile user can be a resource consumer or supplier, depending on whether her local resource is sufficient or not.…”

Smart Room Lighting System for Energy Efficiency in Indoor Environment

“…The IoT is expected to reach 18 billion interconnected devices by 2022 [6]. As a result of these developments in technology, a significant amount of research has been conducted on IoT to improve communication and connect it with human labor [7]. That's why IoT in the smart room lighting system is expected to achieve energy efficiency results.…”

Markov Renewal Prediction and Radial Kronecker Neural Network Based Handover for Seamless Mobility

“…Future generation communication also necessitates reliability, seamless operations, and management of reconfiguration as far as heterogeneous wireless networks are concerned. Object mobility support algorithm was designed based on RSSI to handle seamless mobility [8]. A hybrid deep learning that consisted of convolution neural network (CNN) and long short term memory (LSTM) was presented by Khan et al [9].…”