Design and analysis of adaptive hierarchical low-power long-range networks

A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications

EASND: Energy Adaptive Secure Neighbour Discovery Scheme for Wireless Sensor Networks

Wireless Sensor Network (WSN) is defined as a distributed system of networking, which is enabled with set of resource constrained sensors, thus attempt to providing a large set of capabilities and connectivity interferences. After deployment nodes in the network must automatically affected heterogeneity of framework and design framework steps, including obtaining knowledge of neighbor nodes for relaying information. The primary goal of the neighbor discovery process is reducing power consumption and enhancing the lifespan of sensor devices. The sensor devices incorporate with advanced multi-purpose protocols, and specifically communication models with the pre-eminent objective of WSN applications. This paper introduces the power and security aware neighbor discovery for WSNs in symmetric and asymmetric scenarios. We have used different of neighbor discovery protocols and security models to make the network as a realistic application dependent model. Finally, we conduct simulation to analyze the performance of the proposed EASND in terms of energy efficiency, collisions, and security. The node channel utilization is exceptionally elevated, and the energy consumption to the discovery of neighbor nodes will also be significantly minimized. Experimental results show that the proposed model has valid accomplishment

Powering the Internet of Things Through Light Communication

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Novel solutions are required to connect billions of devices to the network as envisioned by the IoT. In this article we propose to use LiFi, which is based on off-the-shelf LEDs, as an enabler for the IoT in indoor environments. We present LiFi4IoT, a system which, in addition to communication, provides three main services that the radio frequency (RF) IoT networks struggle to offer: precise device positioning; the possibility of delivering power, since energy can be harvested from light; and inherent security due to the propagation properties of visible light. We analyze the application space of IoT in indoor scenarios, and propose a LiFi4IoT access point (AP) that communicates simultaneously with IoT devices featuring different types of detectors, such as CMOS camera sensors, PDs, and solar cells. Based on the capabilities of these technologies, we define three types of energy self-sufficient IoT "motes" and analyze their feasibility. Finally, we identify the main research directions to enable the LiFi4IoT vision and provide preliminary results for several of these.Peer ReviewedPostprint (author's final draft

RPL Cross-Layer Scheme for IEEE 802.15.4 IoT Devices With Adjustable Transmit Power

Article number 9523554We propose a novel cross-layer scheme to reduce energy consumption in wireless sensor networks composed of IEEE 802.15.4 IoT devices with adjustable transmit power. Our approach is based on the IETF’s Routing Protocol for Low power and lossy networks (RPL). Nodes discover neighbors and keep fresh link statistics for each available transmit power level. Using the product of ETX and local transmit power level as a single metric, each node selects both the parent that minimizes the energy for packet transmission along the path to the root and the optimal local transmit power to be used. We have implemented our cross-layer scheme in NG-Contiki using the Z1 mote and two transmit power levels (55mW and 31mW). Simulations of a network of 15 motes show that (on average) 66% of nodes selected the low-power setting in a 25 m × 25 m area. As a result, we obtained an average reduction of 25% of the energy spent on transmission and reception of packets compared to the standard RPL settings where all nodes use the same transmit power level. In large scenarios (e.g., 150 m × 150 m and 40-100 motes), our approach provides better results in dense networks where reducing the transmit power of nodes does not translate into longer paths to the root nor degraded quality of service