Designing Time Slotted Channel Hopping and Information-Centric Networking for IoT

International audienceRecent proposals to simplify the operation of the IoT include the use of Information Centric Networking (ICN) paradigms. While this is promising, several challenges remain. In this paper, our core contributions (a) leverage ICN communication patterns to dynamically optimize the use of TSCH (Time Slotted Channel Hopping), a wireless link layer technology increasingly popular in the IoT, and (b) make IoT-style routing adaptive to names, resources, and traffic patterns throughout the network -- both without cross-layering

A Case for Time Slotted Channel Hopping for ICN in the IoT

Recent proposals to simplify the operation of the IoT include the use of Information Centric Networking (ICN) paradigms. While this is promising, several challenges remain. In this paper, our core contributions (a) leverage ICN communication patterns to dynamically optimize the use of TSCH (Time Slotted Channel Hopping), a wireless link layer technology increasingly popular in the IoT, and (b) make IoT-style routing adaptive to names, resources, and traffic patterns throughout the network--both without cross-layering. Through a series of experiments on the FIT IoT-LAB interconnecting typical IoT hardware, we find that our approach is fully robust against wireless interference, and almost halves the energy consumed for transmission when compared to CSMA. Most importantly, our adaptive scheduling prevents the time-slotted MAC layer from sacrificing throughput and delay

Efficiency enhancement using optimized static scheduling technique in TSCH networks

In recent times, the reliable and real-time data transmission becomes a mandatory requirement for various industries and organizations due to the large utilization of Internet of Things (IoT) devices. However, the IoT devices need high reliability, precise data exchange and low power utilization which cannot be achieved by the conventional Medium Access Control (MAC) protocols due to link failures and high interferences in the network. Therefore, the Time-Slotted Channel Hopping (TSCH) networks can be used for link scheduling under the IEEE 802.15.4e standard. In this paper, we propose an Optimized Static Scheduling Technique (OSST) for the link scheduling in IEEE 802.15.4e based TSCH networks. In OSST the link schedule is optimized by considering the packet latency information during transmission by checking the status of the transmitted packets as well as keeping track of the lost data packets from source to destination nodes. We evaluate the proposed OSST model using 6TiSCH Simulator and compare the different performance metrics with Simple distributed TSCH Scheduling

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application

Delay-Tolerant ICN and Its Application to LoRa

Connecting long-range wireless networks to the Internet imposes challenges due to vastly longer round-trip-times (RTTs). In this paper, we present an ICN protocol framework that enables robust and efficient delay-tolerant communication to edge networks. Our approach provides ICN-idiomatic communication between networks with vastly different RTTs. We applied this framework to LoRa, enabling end-to-end consumer-to-LoRa-producer interaction over an ICN-Internet and asynchronous data production in the LoRa edge. Instead of using LoRaWAN, we implemented an IEEE 802.15.4e DSME MAC layer on top of the LoRa PHY and ICN protocol mechanisms in RIOT OS. Executed on off-the-shelf IoT hardware, we provide a comparative evaluation for basic NDN-style ICN [60], RICE [31]-like pulling, and reflexive forwarding [46]. This is the first practical evaluation of ICN over LoRa using a reliable MAC. Our results show that periodic polling in NDN works inefficiently when facing long and differing RTTs. RICE reduces polling overhead and exploits gateway knowledge, without violating ICN principles. Reflexive forwarding reflects sporadic data generation naturally. Combined with a local data push, it operates efficiently and enables lifetimes of >1 year for battery powered LoRa-ICN nodes.Comment: 12 pages, 7 figures, 2 table

An energy efficient routing scheme by using GPS information for wireless sensor networks

In the process of transmission in wireless sensor networks (WSN), a vital problem is that a centre region close to the sink will form in which sensors have to cost vast amount of energy. To communicate in an energy-efficient manner, compressed sensing (CS) has been employed gradually. However, the performance of plain CS is significantly dependant on the specific data gathering strategy in practice. In this paper, we propose an energy-efficient data gathering scheme based on regionalisation CS. Subsequently, advanced methods for practical applications are considered. Experiments reveal that our scheme outperforms distributed CS, the straight forward and the mixed schemes by comparing different parameters of the data package, and the considered methods also guarantee its feasibility.N/

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

A Platform for Large-Scale Regional IoT Networks

The Internet of Things (IoT) promises to allow everyday objects to connect to the Internet and interact with users and other machines ubiquitously. Central to this vision is a pervasive wireless communication network connecting each end device. For individual IoT applications it is costly to deploy a dedicated network or connect to an existing cellular network, especially as these applications do not fully utilize the bandwidth provided by modern high speeds networks (e.g., WiFi, 4G LTE). On the other hand, decades of wireless research have produced numerous low-cost chip radios and effective networking stacks designed for short-range communication in the Industrial, Scientific and Medical Radio band (ISM band). In this thesis, we consider adapting this existing technology to construct shared regional low-powered networks using commercially available ISM band transceivers. To maximize network coverage, we focus on low-power wide-area wireless communication which enables links to reliably cover 10 km or more depending on terrain transmitting up to 1 Watt Equivalent Isotropically Radiated Power (EIRP). With potentially thousands of energy constrained IoT devices vying for extremely limited bandwidth, minimizing network coordination overhead and maximizing channel utility is essential. To address these challenges, we propose a distributed queueing (DQ) based MAC protocol, DQ-N. DQ-N exhibits excellent performance, supporting thousands of IoT devices from a single base station. In the future, these networks could accommodate a heterogeneous set of IoT applications, simplifying the IoT application development cycle, reducing total system cost, improving application reliability, and greatly enhancing the user experience