Analogous to the way humans use the Internet, devices will be the main users in the Internet of Things (IoT) ecosystem. Therefore, device-to-device (D2D) communication is expected to be an intrinsic part of the IoT. Devices will communicate with each other autonomously without any centralized control and collaborate to gather, share, and forward information in a multihop manner. The ability to gather relevant information in real time is key to leveraging the value of the IoT as such information will be transformed into intelligence, which will facilitate the creation of an intelligent environment. Ultimately, the quality of the information gathered depends on how smart the devices are. In addition, these communicating devices will operate with different networking standards, may experience intermittent connectivity with each other, and many of them will be resource constrained. These characteristics open up several networking challenges that traditional routing protocols cannot solve. Consequently, devices will require intelligent routing protocols in order to achieve intelligent D2D communication. We present an overview of how intelligent D2D communication can be achieved in the IoT ecosystem. In particular, we focus on how state-of-the-art routing algorithms can achieve intelligent D2D communication in the IoT.

Intelligent Device-to-Device Communication in the Internet of Things

Remote measurement of product core temperature is an important prerequisite to improve the cool chain of food products and reduce losses. This paper examines and shows possible solutions to technical challenges that still hinder practical applications of wireless sensor networks in the field of food transport supervision. The high signal attenuation by water-containing products limits the communication range to less than 0.5 m for the commonly used 2.4 GHz radio chips. By theoretical analysis of the dependency of signal attenuation on the operating frequency, we show that the signal attenuation can be largely reduced by the use of 433 MHz or 866 MHz devices, but forwarding of messages over multiple hops inside a sensor network is mostly unavoidable to guarantee full coverage of a packed container. Communication protocols have to provide compatibility with widely accepted standards for integration into the global Internet, which has been achieved by programming an implementation of the constrained application protocol for wireless sensor nodes and integrating into IPv6-based networks. The sensor's battery lifetime can be extended by optimizing communication protocols and by in-network pre-processing of the sensor data. The feasibility of remote freight supervision was demonstrated by our full-scale ‘Intelligent Container’ prototype.

https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2013.0304

Communication techniques and challenges for wireless food quality monitoring.

RPL (IPv6 Routing Protocol for Low Power and Lossy networks) is a routing protocol recently standardized by the IETF. RPL has been designed to operate in energy-constrained networks with thousands of nodes, and therefore it is one of the most promising candidate routing protocols for Advanced Metering Infrastructure (AMI) networks. In this paper a performance evaluation of RPL is presented. An extensive study of the protocol is carried out with particular focus on Trickle, the algorithm adopted to control routing update distribution across the network. The performance of the protocol is analyzed considering different Trickle parameters in order to capture their impact on route formation and node power consumption. Results highlight that the nondeterministic nature of Trickle can lead to sub-optimal route formation especially when high message suppression is per-formed. In order to mitigate this issue, an enhanced version of the protocol, namely Trickle-F, is proposed in order to guarantee fair broadcast suppression. Trickle-F is demonstrated to be effective in obtaining more efficient routes with the same power consumption of the original version.

Trickle-F: Fair broadcast suppression to improve energy-efficient route formation with the RPL routing protocol

The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently developed by the Internet Engineering Task Force (IETF). Given its crucial role in enabling the Internet of Things, a significant amount of research effort has already been devoted to RPL. However, the RPL network convergence process has not yet been investigated in detail. In this paper we study the influence of the main RPL parameters and mechanisms on the network convergence process of this protocol in IEEE 802.15.4 multihop networks. We also propose and evaluate a mechanism that leverages an option available in RPL for accelerating the network convergence process. We carry out extensive simulations for a wide range of conditions, considering different network scenarios in terms of size and density. Results show that network convergence performance depends dramatically on the use and adequate configuration of key RPL parameters and mechanisms. The findings and contributions of this work provide a RPL configuration guideline for network convergence performance tuning, as well as a characterization of the related performance trade-offs.

https://www.mdpi.com/1424-8220/14/7/11993/pdf

On the network convergence process in RPL over IEEE 802.15.4 multihop networks: improvement and trade-offs.

Routing Protocol for Low Power and Lossy networks (RPL) is the de-facto standard for routing in resource-constrained low-power and lossy networks (LLNs) which represent the building block for the ever-growing Internet of Things (IoT). RPL along with other routing protocols make the deployment of Trickle algorithm as a mechanism for controlling and maintaining the routing traffic frequency. Trickle employs two different techniques to manage the routing traffic efficiently. Firstly, the suppression phase where a node suppresses its control data transmission if it is redundant. Secondly, the traffic frequency adaptation, where the nodes increase their sending rate when discovering inconsistency while slow down their rate as the network becomes stable. The efficiency of Trickle has been approved in terms of power consumption and scalability, however, imposing the so-called listen-only period as a solution for the short-listen problem comes at the expense of increased convergence time. Thus, in this paper, an enhanced version of Trickle is proposed, namely, E-Trickle which solves the short-listen problem without imposing the listen-only period. Simulation results show that E-Trickle have decreased the convergence time by up to 43% while preserving the same efficiency of Trickle in terms of power consumption, scalability and reliability.

E-Trickle: Enhanced Trickle Algorithm for Low-Power and Lossy Networks

The Trickle algorithm has proven to be of great benefit to the Wireless Sensor Networking area. It has shown general applicability in this field, e.g. for code distribution to smart objects and routing information distribution between smart objects. Up to now analysis of the algorithm has focussed on simulation studies and measurement campaigns. This paper introduces an analytical models for the algorithm’s behaviour for the time to consistency. The model is compared with simulation results for a set of network topologies and enables to discover efficient settings of the algorithm for various application areas, such as logistics.

/pdf/modelling-and-simulating-the-trickle-algorithm-551oxt2uku.pdf

Modelling and Simulating the Trickle Algorithm

As networks become more dynamic, the awareness of the network members’ current context is of growing importance. This can be said especially for communication networks, but also for other types of networks, e.g. logistic networks. Context-aware routing takes this context information into account when selecting routes in such networks. This paper introduces a novel generic framework for context-aware routing, including the message flow as well as the route decision method. The framework is not limited to a single application domain, but it is a generalized approach that can be adapted and specialized for various application domains. As an example, a wireless sensor network scenario is presented where this routing framework is applied.

/pdf/a-generic-framework-for-context-aware-routing-and-its-imple-4z1pnelyur.pdf

A Generic Framework for Context-Aware Routing and its Imple- mentation in Wireless Sensor Networks

Diese Veroffentlichung beschreibt einen neuartigen Ansatz zur Umsetzung von Selbststeuerung in der Transportlogistik. Selbststeuerung ist notwendig, um die Komplexitat heutiger und zukunftiger Lieferketten handhabbar zu gestalten. Diese Selbststeuerung bedingt Mobilitat und deren Aspekte reichen von mobiler Kommunikation uber die Migrationsfahigkeit von Softwareagenten bis hin zur Kopplung zwischen realer und virtueller Welt, also der Mobilitat der Information. Durch den Einsatz modernster IuK-Technologien wie RFID, Sensornetze, mobile Kommunikationsnetze und Softwareagenten werden diese Konzepte im Rahmen eines Demonstratorsystems fur Selbststeuerung in der Logistik umgesetzt.

/pdf/ein-multi-agentensystem-fur-selbststeuerung-in-der-2zf1aw4ii1.pdf

Ein Multi-Agentensystem für Selbststeuerung in der Transportlogistik

Massively distributed clouds (MDC) have a huge potential in serving novel applications and services in many situations. Mainly, they are able to provide communication without the use of an infrastructure and to guarantee full data and user anonymity. However, their implementation is not trivial and requires innovation in many different fields, such as opportunistic communications, big data management and security. In this paper, we present our first design of a MDC supporting architecture, called DICE: Distributed Infrastructureless Cloud sErvices. We present our main application scenario and focus on implementation challenges and early results.

Carmelita Görg

Papers

Modelling and Simulating the Trickle Algorithm

A Generic Framework for Context-Aware Routing and its Imple- mentation in Wireless Sensor Networks

Modelling and Simulating the Trickle Algorithm

Ein Multi-Agentensystem für Selbststeuerung in der Transportlogistik

DICE: A Novel Platform to Support Massively Distributed Clouds