Detailed examination of a packet collision model for Bluetooth Low Energy advertising mode

The aim of this paper is to investigate the amount of energy that is required to successfully transmit information inside the Bluetooth Low Energy (BLE) advertising packets. There are applications that require more than one BLE node to simultaneously transmit data. The BLE protocol utilizes a specific communication method termed advertising mode to perform unidirectional broadcasts of data from the advertising devices. However, with an increased number of BLE devices advertising simultaneously, there will be inevitable packet collisions from the advertising devices. This results in a waste of energy, specifically in low-power applications where lower consumption is desirable to minimize the need for battery replacements. This paper examines a packet collision model for the BLE advertising mode with the results validated using experimental data. Our analysis shows that when the throughput of the BLE network starts to fall due to an increase in the number of packet collisions, the energy consumption of the BLE nodes increase exponentially with respect to the number of nodes

A BLE-based multi-gateway network infrastructure with handover support for mobile BLE peripherals

Bluetooth Low Energy (BLE) is a popular technology within the Internet of Things. It allows low-power, star networks to be set up between a BLE gateway and multiple, power-constrained BLE devices. However, these networks tend to be static, not supporting BLE devices that can freely move around in an environment of multiple interconnected BLE gateways and perform handovers whenever necessary. This work proposes two alternative network architectures for mobile BLE peripherals. One leverages on IPv6 over BLE, whereas the other combines default BLE mechanisms with an additional custom controller. On top, we study in detail the handover mechanism that must be present in both architectures and compare the performance of both a passive and active handover approach. The passive handover approach can be set up without any extra implementation, but an active handover approach offers more proactive handover decisions and can provide a much lower handover latency. All proposed solutions have been implemented and validated on real hardware, showing the feasibility of having future infrastructures with support for mobile BLE devices

The Bluetooth Mesh Standard: An Overview and Experimental Evaluation

Mesh networks enable a many-to-many relation between nodes, which means that each node in the network can communicate with every other node using multi-hop communication and path diversity. As it enables the fast roll-out of sensor and actuator networks, it is an important aspect within the Internet of Things (IoT). Utilizing Bluetooth Low Energy (BLE) as an underlying technology to implement such mesh networks has gained a lot of interest in recent years. The result was a variety of BLE meshing solutions that were not interoperable because of the lack of a common standard. This has changed recently with the advent of the Bluetooth Mesh standard. However, a detailed overview of how this standard operates, performs and how it tackles other issues concerning BLE mesh networking is missing. Therefore, this paper investigates this new technology thoroughly and evaluates its performance by means of three approaches, namely an experimental evaluation, a statistical approach and a graph-based simulation model, which can be used as the basis for future research. Apart from showing that consistent results are achieved by means of all three approaches, we also identify possible drawbacks and open issues that need to be dealt with

Analytical and experimental performance evaluation of BLE neighbor discovery process including non-idealities of real chipsets

The purpose of this paper is to evaluate from a real perspective the performance of Bluetooth Low Energy (BLE) as a technology that enables fast and reliable discovery of a large number of users/devices in a short period of time. The BLE standard specifies a wide range of configurable parameter values that determine the discovery process and need to be set according to the particular application requirements. Many previous works have been addressed to investigate the discovery process through analytical and simulation models, according to the ideal specification of the standard. However, measurements show that additional scanning gaps appear in the scanning process, which reduce the discovery capabilities. These gaps have been identified in all of the analyzed devices and respond to both regular patterns and variable events associated with the decoding process. We have demonstrated that these non-idealities, which are not taken into account in other studies, have a severe impact on the discovery process performance. Extensive performance evaluation for a varying number of devices and feasible parameter combinations has been done by comparing simulations and experimental measurements. This work also includes a simple mathematical model that closely matches both the standard implementation and the different chipset peculiarities for any possible parameter value specified in the standard and for any number of simultaneous advertising devices under scanner coverage

Fog-Driven Context-Aware Architecture for Node Discovery and Energy Saving Strategy for Internet of Things Environments

The consolidation of the Fog Computing paradigm and the ever-increasing diffusion of Internet of Things (IoT) and smart objects are paving the way toward new integrated solutions to efficiently provide services via short-mid range wireless connectivity. Being the most of the nodes mobile, the node discovery process assumes a crucial role for service seekers and providers, especially in IoT-fog environments where most of the devices run on battery. This paper proposes an original model and a fog-driven architecture for efficient node discovery in IoT environments. Our novel architecture exploits the location awareness provided by the fog paradigm to significantly reduce the power drain of the default baseline IoT discovery process. To this purpose, we propose a deterministic and competitive adaptive strategy to dynamically adjust our energy-saving techniques by deciding when to switch BLE interfaces ON/OFF based on the expected frequency of node approaching. Finally, the paper presents a thorough performance assessment that confirms the applicability of the proposed solution in several different applications scenarios. This evaluation aims also to highlight the impact of the nodes' dynamic arrival on discovery process performance

Algorithms to Find Two-Hop Routing Policies in Multiclass Delay Tolerant Networks

Most of the literature on delay tolerant networks (DTNs) focuses on optimal routing policies exploiting a priori knowledge about nodes mobility traces. For the case in which no a priori knowledge is available (very common in practice), apart from basic epidemic routing, the main approaches focus on controlling two-hop routing policies. However, these latter results commonly employ fluid approximation techniques, which, in principle, do not provide any theoretical bound over the approximation ratio. In our work, we focus on the case without a priori mobility knowledge and we provide approximation algorithms with theoretical guarantees that can be applied to cases where the number of hops allowed in the routing process is arbitrary. Our approach is rather flexible allowing us to address heterogeneous mobility patterns and transmission technologies, to consider explicitly the signaling and transmission costs, and to include also nodes discarding packets after a local timeout. We then provide a comprehensive performance evaluation of our algorithms, showing that two-hop routing provides the best tradeoff between delay and energy and that, in this case, they find solutions very close to the optimal ones with a low overhead. Finally, we compare our methods against some state-of-the-art approaches by means of a DTN simulation environment in realistic settings

Model for Predicting Bluetooth Low Energy Micro-Location Beacon Coin Cell Battery Lifetime

Bluetooth Low Energy beacon devices, typically operating on coin cell batteries, have emerged as key components of micro-location wireless sensor networks. To design efficient and reliable networks, designers require tools for predicting battery and beacon lifetime, based on design parameters that are specific to micro-location applications. This design science research contributes to the implementation of an artifact functioning as a predictive tool for coin cell battery lifetime when powering Bluetooth Low Energy beacon devices. Building upon effective and corroborated components from other researchers, the Beacon Lifetime Model 1.0 was developed as a spreadsheet workbook, providing a user interface for designers to specify parameters, and providing a predictive engine to predict coin cell battery lifetime. Results showed that the measured and calculated predictions were consistent with those derived through other methodologies, while providing a uniquely extensible user interface which may accommodate future work on emerging components. Future work may include research on real world scenarios, as beacon devices are deployed for robust micro-location applications. Future work may also include improved battery models that capture increasingly accurate performance under micro-location workloads. Beacon Lifetime Model 1.x is designed to incorporate those emerging components, with Beacon Lifetime Model1.0 serving as the initial instantiation of this design science artifact