While the Internet of Things (IoT) is driving a transformation of current society toward a smarter one, new challenges and opportunities have arisen to accommodate the demands of IoT development. Low power wireless devices are, undoubtedly, the most viable solution for diverse IoT use cases. Among such devices, Bluetooth low energy (BLE) beacons have emerged as one of the most promising due to the ubiquity of Bluetooth-compatible devices, such as iPhones and Android smartphones. However, for BLE beacons to continue penetrating the IoT ecosystem in a holistic manner, interdisciplinary research is needed to ensure seamless integration. This paper consolidates the information on the state-of-the-art BLE beacon, from its application and deployment cases, hardware requirements, and casing design to its software and protocol design, and it delivers a timely review of the related research challenges. In particular, the BLE beacon’s cutting-edge applications, the interoperability between packet profiles, the reliability of its signal detection and distance estimation methods, the sustainability of its low energy, and its deployment constraints are discussed to identify research opportunities and directions.

BLE Beacons for Internet of Things Applications: Survey, Challenges, and Opportunities

The current social impact of new technologies has produced major changes in all areas of society, creating the concept of a smart city supported by an electronic infrastructure, telecommunications and information technology. This paper presents a review of Bluetooth Low Energy (BLE), Near Field Communication (NFC) and Visible Light Communication (VLC) and their use and influence within different areas of the development of the smart city. The document also presents a review of Big Data Solutions for the management of information and the extraction of knowledge in an environment where things are connected by an “Internet of Things” (IoT) network. Lastly, we present how these technologies can be combined together to benefit the development of the smart city.

https://www.mdpi.com/1424-8220/16/11/1968/pdf

State of the Art, Trends and Future of Bluetooth Low Energy, Near Field Communication and Visible Light Communication in the Development of Smart Cities.

This paper presents a simple concept which has not been, up to now, thoroughly explored in scientific research: the use of information coming from the network of Internet connected mobile devices (on vehicles) to regulate traffic light systems. Three large-scale changes are going to shape the future of transportation and could lead to the regulation of traffic signal system based on floating car data (FCD): (i) the implementation of Internet connected cars with global navigation satellite (GNSS) system receivers and the autonomous car revolution; (ii) the spreading of mobile cooperative Web 2.0 and the extension to connected vehicles; (iii) an increasing need for sustainability of transportation in terms of energy efficiency, traffic safety, and environmental issues. Up to now, the concept of floating car data (FCD) has only been extensively used to obtain traffic information and estimate traffic parameters. Traffic lights regulation based on FCD technology has not been fully researched since the implementation requires new ideas and algorithms. This paper intends to provide a seminal insight into the important issue of adaptive traffic light based on FCD by presenting ideas that can be useful to researchers and engineers in the long-term task of developing new algorithms and systems that may revolutionize the way traffic lights are regulated.

/pdf/the-use-of-adaptive-traffic-signal-systems-based-on-floating-41oucc8n5w.pdf

The Use of Adaptive Traffic Signal Systems Based on Floating Car Data

Bluetooth low energy (BLE) beacons have been widely deployed to deliver proximity-based services (PBSs) to user’s smartphones when users are in the proximity of a beacon. Conventional proximity detection simply uses the received signal strength (RSS) to infer the proximity, and then retrieves the PBS by mapping the beacon ID with the corresponding service in the cloud database. Such an approach suffers two major issues: 1) the severe RSS fluctuation might confuse the smartphone during the detection and 2) a malicious PBS can be delivered by manipulating the same beacon ID. This paper proposes RF fingerprinting to label a beacon with an $N$ -dimensional fingerprint vector, which consists of $N$ RSS values from $N$ deployed beacons. The contribution of our proposed method is twofold: 1) we infer the proximity based on the fingerprint vector instead of relying solely on the single RSS value and 2) we retrieve the PBS by mapping the fingerprint vector instead of the hard-coded beacon ID. The challenge with our proposed approach is the incomplete fingerprint observation during real-time detection, resulting in an underdetermined proximity detection problem. To this end, we exploit the compressive sensing (CS) approach based on the differential evolutional algorithm to address such an underdetermined problem. Extensive simulations with real-world datasets show that our proposed approach outperforms the legacy machine learning techniques with substantial performance gains.

A Compressive Sensing Approach to Detect the Proximity Between Smartphones and BLE Beacons

With the proliferation of mobile devices having BLE capability and the introduction of Beacon technology, crowdsourcing-based approaches have recently emerged as a promising solution for localization of lost objects or individuals (e.g., children or elders). By attaching affordable Beacon tags to them, objects of care could be tracked and localized by user devices in the proximity. While crowd GPS service has gained popularity recently, it has not extended beyond passive mode in which localization is achieved in the background without intruding the mobility of users. In this paper, we study the localization of lost objects through the crowd GPS service in an active manner. We propose clustering users in a Beacon tag network based on the benefits they can receive from each other in terms of the localization of their lost items. A new metric is developed to quantify this benefit and the users that can provide most of the total possible benefits to each other are then grouped together so that they can provide active localization service for only the users most beneficial to them. The clustering of users is achieved based on both a greedy heuristic based algorithm and a genetic algorithm. Extensive simulation results are conducted utilizing both synthetic data and real location based social network datasets. The results show the effective partitioning of the users under different user counts and groups while valuing the privacy of users at its maximum by limiting the number of interactions between users.

Clustered Crowd GPS for Privacy Valuing Active Localization

This paper presents a concept of an extended iBeacon system for proximity target marketing. iBeacon is a proximal wireless notification service proposed by Apple, Inc. This service uses beacon modules emitting radio waves based on Bluetooth Low Energy technology and provides automatic triggering of a single notification from a beacon module to multiple smart phones at proximity. In usual iBeacon systems, there is one-to-one correspondence between notification and beacon module. However, if we introduce a memory to remember the history of iBeacon detection, iBeacon systems can be extended to handle multiple notifications by multiple beacon
modules with awareness of some contexts of pedestrians’ moving directions, which enables a guide, for example, to the exit of buildings. We explain this idea for proximity target marketing using iBeacon and discuss its implementation with our simple experiment and performance evaluation. We are going to explain what it is and how it works, in order to understand how the app works and how it uses these potentialities. This new technology may also be used by Android system. iBeacon works with Bluetooth Low Energy (BLE), also known as Bluetooth 4.0 or intelligent Bluetooth. We show in this project how iBeacon technically really work, The app. Smart devices, such as Android phone can detect advertising signals sent by the low energy consumption tiny device. Notifications will be pushed automatically to the user when smart device with iBeacon Apps come to certain areas. It provides promising and portfolio usage scenarios for business, such as retail stores, to push related information about customers’ interested items with the awareness of customers’ location.

http://www.ijecs.in/issue/v5-i5/8%20ijecs.pdf

Balasaheb Nawale

Papers

Beacon for Proximity Target Marketing