Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Networks in the Internet of Things

Participants: Soukaina Cherkaoui, Alexis Duque, Guillaume Gaillard, Hervé Rivano, Razvan Stanica, Fabrice Valois.

Service Level Agreements in the Internet of Things

With the growing use of distributed wireless technologies for modern services, the deployments of dedicated radio infrastructures do not enable to ensure large-scale, low-cost and reliable communications. The Ph.D. thesis of Guillaume Gaillard [2] aims at enabling an operator to deploy a radio network infrastructure for several client applications, hence forming the Internet of Things (IoT). We evaluate the benefits earned by sharing an architecture among different traffic flows, in order to reduce the costs of deployment, obtaining a wide coverage through efficient use of the capacity on the network nodes. We thus need to ensure a differentiated Quality of Service (QoS) for the flows of each application.

We propose to specify QoS contracts, namely Service Level Agreements (SLAs), in the context of the IoT. SLAs include specific Key Performance Indicators (KPIs), such as the transit time and the delivery ratio, concerning connected devices that are geographically distributed in the environment. The operator agrees with each client on the sources and amount of traffic for which the performance is guaranteed. Secondly, we describe the features needed to implement SLAs on the operated network, and we organize them into an SLA management architecture. We consider the admission of new flows, the analysis of current performance and the configuration of the operator’s relays. Based on a robust, multi-hop technology, IEEE Std 802.15.4-2015 TSCH mode, we provide two essential elements to implement the SLAs : a mechanism for the monitoring of the KPIs [19], and KAUSA, a resource allocation algorithm with multi-flow QoS constraints [18]. The former uses existing data frames as a transport medium to reduce the overhead in terms of communication resources. We compare different piggybacking strategies to find a tradeoff between the performance and the efficiency of the monitoring. With the latter, KAUSA, we dedicate adjusted time-frequency resources for each message, hop by hop. KAUSA takes into account the interference, the reliability of radio links and the expected load to improve the distribution of allocated resources and prolong the network lifetime [17]. We show the gains and the validity of our contributions with a simulation based on realistic traffic scenarios and requirements.

Channel Access in Machine-to-Machine Communications

The densification of the urban population and the rise of smart cities applications foster the need for capillary networks collecting data from sensors monitoring the cities. Among the multiple networking technologies considered for this task, cellular networks, such as LTE-A, bring an ubiquitous coverage of most cities. It is therefore necessary to understand how to adapt LTE-A, and what should be the future 5G architecture, in order to provide efficient connectivity to Machine-to-Machine (M2M) devices alongside the main target of mobile networks, Human-to-Human devices. Indeed, cellular random access procedures are known to suffer from congestion in presence of a large number of devices, while smart cities scenarios expect huge density of M2M devices. Several solutions have been investigated for the enhancement of the current LTE-A access management strategy. In [14], we contribute to the modeling and computation of the capacity of the LTE-A Random Access Channel (RACH) in terms of simultaneous successful access. In particular, we investigate the hypothesis of piggybacking the payload of Machine Type Communications from M2M devices within the RACH, and show that M2M densities considered realistic for smart cities applications are difficult to sustain by the current LTE-A architecture.

Visible Light Communications in the Internet of Things

The Internet of Things connects devices, such as everyday consumer objects, enabling information gathering and improved user experience. Also, this growing and dynamic market makes that consumers nowadays expect electronic products, even the cheapest, to include wireless connectivity. However, despite the fact that radio based solutions exist, such as Bluetooth Low Energy, the manufacturing costs introduced by these radio technologies are non-negligible compared to the initial product price. As most of the home electronics already integrate small light emitting diodes, Visible Light Communication appears as a competitive alternative. However, its broad adoption is suffering from a lack of integration with smartphones, which represent the communication hubs for most of the users. To overcome this issue, in [16], we propose a line of sight LED-to-camera communication system based on a small color LED and a smartphone. We design a cheap prototype as proof of concept of a near communication framework for the Internet of Things. We evaluate the system performance, its reliability and the environment influence on the LED-to-camera communication, highlighting that a throughput of a few kilobits per second is reachable. Finally, we design a real time, efficient LED detection and image processing algorithm to leverage the specific issues encountered in the system.

Radio Frequency Identification in Dense Environments

Radio Frequency Identification (RFID) is another cheap technology shaping the Internet of Things. The rapid development of RFID has allowed its large adoption and led to increasing deployments of RFID solutions in diverse environments under varying scenarios and constraints. The nature of these constraints ranges from the amount to the mobility of the readers deployed, which in turn highly affects the quality of the RFID system, causing reading collisions. However, the technology suffers from a recurring issue: the reader-to-reader collisions. Numerous protocols have been proposed to attempt to reduce them, but remaining reading errors still heavily impact the performance and fairness of dense RFID deployments.

In order to ensure collision-free reading, a scheduling scheme is needed to read tags in the shortest possible time. In [25], we study this scheduling problem in a stationary setting and the reader minimization problem in a mobile setting. We show that the optimal schedule construction problem is NP-complete and provide an approximation algorithm that we evaluate our techniques through simulation. Moving closer to practical solutions, [20] introduces a new Distributed Efficient & Fair Anticollision for RFID (DEFAR) protocol. DEFAR reduces both monochannel and multichannel collisions, as well as interference, by a factor of almost 90% in comparison with the best state of the art protocols. The fairness of the medium access among the readers is improved to a 99% level. Such improvements are achieved by applying a TDMA-based "server-less" approach and assigning different priorities to readers depending on their behavior over precedent rounds. A distributed reservation phase is organized between readers with at least one winning reader afterwards. Then, multiple reading phases occur within a single frame in order to obtain fast coverage and high throughput. The use of different reader priorities based on reading behaviors of previous frames also contributes to improve both fairness and efficiency.

Another type of collisions appears when the RFID tags are not only dense, but also mobile. mDEFAR [21] is an adaptation of DEFAR, while CORA [7] is more of a locally mutual solution where each reader relies on its neighborhood to enable itself or not. Using a beaconing mechanism, each reader is able to identify potential (non-)colliding neighbors in a running frame and as such chooses to read or not. Performance evaluation shows high performance in terms of coverage delay for both proposals quickly achieving 100% coverage depending on the considered use case while always maintaining consistent efficiency levels above 70%. Compared to the state of the art, our solutions proved to be better suited for highly dense and mobile environments, offering both higher throughput and efficiency. The results reveal that depending on the application considered, choosing either mDEFAR or CORA helps improve efficiency and coverage delay.