The Detection Data Processing Mechanism for Vehicular Cyber Physical System in IoT Environment

The use of the Internet-controlled devices has become more ubiquitous. The Internet of Things (IoT) has been developed in the sense that it is offered as a monitoring or management system but also as an ability to manage individual devices (things). Devices with already integrated connection protocols are already offered on market, as well as components that can upgrade not IoT devices. In this paper, the author examines such devices, that is, components that upgrade the already existing devices, for the purpose of managing their operation. The paper analyses the cost side of the device, where annual electricity costs are given for three IoT devices and three Internet Access Point devices for connecting devices to the Internet, i.e., to further rented services. The paper also offers an analysis of the attitudes of device users on knowledge of the possibilities, costs as well as security risks. According to the analysis, cost increases and security breaches are eligible to enable remote monitoring and management of devices.

Section: Related Workmentioning

confidence: 99%

Cost and Security Aspects of the System for Monitoring and Managing Things Over the Internet

2023

“…For sharing data, IoT devices rely on low-power connectivity protocols and limited computational capabilities. [4,27]. IoT sensors often have battery life, reliability, memory, etc.…”

Section: Introductionmentioning

confidence: 99%

Novel Geodetic Fuzzy Subgraph-Based Ranking for Congestion Control in RPL-IoT Network

2024

Congestion control is among the most challenging tasks in enhancing QoS in the Internet of Things (IoT). Currently, wireless networks are able to have a large number of connections but with a limited amount of network resources. Consequently, congestion occurs, which adversely affects throughput, transmission delay, packet losses, power consumption management, and the lifespan of a network. This is certainly relevant in networks where transmissions are controlled by the Routing Protocol for Low-Power and Lossy Networks (RPL), which is commonly employed in the Internet of Things network. To solve this problem, a novel Geodetic fuzzy subgraph-based ranking (GFSR-RPL) for congestion control is proposed. Initially, the proposed GFSR-RPL selects the cluster head using K-means clustering. Then the rank calculation can be done via the final route setting for data transmission. A route setup scheme consists of three elements: 1) a Round Trip Time (RTT) estimator that assesses congestion conditions in a variety of ways; 2) a trend and relative strength indicator analysis; and 3) a geodetic fuzzy subgraph rank calculation method that calculates initial RTO (initial retransmission timeouts) accurately. The proposed GFSR-RPL method reduces the energy consumption of up to 43.58%, 25.8%, 14.82% and 6.85% than existing methods such as RPR, CBR-RPL, ACW and ECLRPL.

“…The emergence of the Internet of Things (IoT) is attributed to the strides made in sensor networks and ubiquitous computing, and it refers to intelligent technologies and the service environment that enable autonomous communications [1,2]. In addition, multiple accessible sensors or flexible instruments can calculate human biological measurements directly by simple contact, including the heartbeat (HR), the breath rate (RR), and circulatory strain (BP).…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Based Reconfigurable SIMD Processor for High-Speed End Devices in FPGA

2023

This research article proposed the reconfigurable Single Instruction Multi Data (SIMD) processor design to speed up the accelerated computing task in IoT operations. Single Instruction Multi Data models leverage the parallel real source to speed up computing accelerated tasks. It proposes the utilization of reconfigurable Kogge Stone-dependent hybrid adder structures, now referred to as KS-CPA, in which reconfiguration occurs during the addition operation. The Least Significant Bits (LSB) are processed using a carry propagate adder, while the Most Significant Bits (MSB) are computed using the Kogge Stone adder. Depending on the data width and deviceaccessible energy resources, the hybrid configuration of the adder offers the 4-bit, 8-bit, and 16-bit addition. The adder form is identified by a shift in the configuration of its Carry Look-ahead and then by a Kogge Stone Adder (KSA). Throughout the activity, the KS-CLA crossbreed configuration is used to attain the fastest speed and low energy usage. The effectiveness, including its proposed hybrid adder, is evaluated by looking at the speed, energy, and area parameters, including a suitable area use during rapid applications in which both less delay and low power adders are required. Considering these, we are structuring an IoT processor that can be reconfigured to gain from SIMD. We have demonstrated that our hybrid adder-enhanced processor saves energy up to 13% and reduces 27% latency. The proposed 16 and 32-bit adders will boost time, power, and Area Delay Product (ADP) by almost 18-24% and 13-19% respectively.