Moving Beyond IOT with the Internet of Behaviors | HBKU

Moving Beyond the Internet of Things with the Internet of Behaviors

02 Feb 2021

By Ala Al-Fuqaha and Kashif Ahmad

Moving Beyond the Internet of Things with the Internet of Behaviors

Conscious advertising may be a thing of the past, thanks to the introduction of technologies that can now influence human action. The increasingly interconnected world we live in, the fast and demanding pace of everyday life, and the ubiquity of smart devices mean that influential technologies are inevitably coming. 

What we know about the Internet of Things

The Internet of Things (IoT) refers to a network of Internet-Connected Objects (ICOs). ICOs are created by augmenting physical objects with sensing, actuation, computing, and communication capabilities, which transforms otherwise physical objects into smart ones. Examples of IoT objects include smart onesies, pillows, watches, toothbrushes, and other everyday gadgets. It is estimated that more than 31 billion IoT devices have been deployed around the world to-date, with an average of 2.5 devices per person. This number is projected to grow rapidly in the coming years. The pressing deployment of IoT devices has led to a hyperconnected world where people spend most of their time in close proximity to IoT devices that can monitor and track individual or group behaviors. In turn, these devices have enabled the collection of a staggering amount of data that is needed to develop machine-learning models. 

The race towards data collection has been further motivated by success stories of high-tech companies and the influx of revenue from the sale of raw data, as well as derivative services that are based on data collection. Unfortunately, research has shown that much of the data that is being collected around the world will never be used unless it is immediately allocated. Still, companies are collecting all forms of data, including structured and unstructured data, in a wide range of modalities that range from text to images to video. Ultimately, their data-collection efforts prove to be successful because of the Fear of Missing Out (FOMO) on future monetization opportunities. Data, as such, presents a goldmine of opportunities for high-tech companies.

Influencing behaviors

Kashif AhmadThe ability of IoT devices to infer individual or group habits, interests, preferences, and psychological states has very recently been given a name. The Internet of Behaviors (IoB) has been identified by Gartner as one of the top technological trends of 2021. An important theme behind IoB is to process, analyze, and link the data generated and collected in IoT deployments with individuals to reveal their behavioral patterns when it comes to purchasing, brand preferences, and other commercially-driven activities. Such behavioral analysis, facilitated by IoB, is a futuristic marketing tool that allows businesses to analyze, perceive, and understand consumer needs, demands, and the choices they have been striving for. While online shopping, in-store shopping, commuting, entertainment, and exercise behaviors are archetypal applications of the IoB, many other applicable themes are envisioned in the coming years. 

No doubt, the IoB can help users and businesses though convenience and efficiency applications. It also empowers them to lead and dominate their respective markets. At its core, IoB combines several data and network science technologies and techniques aiming to collect, analyze, and nudge individual and group behaviors. These techniques include computer vision (smart technologies that enable the detection of objects, faces, activities, and impression recognition); natural language processing (such as sentiment analysis for analytics), location tracking; social networks, big data and, predictive analyses, and behavioral nudging strategies. IoB technologies not only identify individual and group behaviors, but also aim to nudge behaviors towards a desired outcome.

Real-world applications and challenges

In the real world, IoB can help the fashion industry to benefit from street images by detecting and gauging perceptions of fashion trends (group behavior), which allows for customized designs. Similarly, influential users on social media platforms can be identified and targeted to receive products at reduced process, so that they can influence their followers to purchase similar products. In order to utilize collected behavioral data to its full potential, businesses need to invest in IoT deployments, data management infrastructures, and Artificial Intelligence (AI) solutions and resources, to be poised to analyze and extract meaningful behavioral insights from the data. 

Uber provides another important success story, where data is collected from the smart phones of drivers and passengers. Their locations and preferences are analyzed to further improve customers’ experience and the monetization of their services. There are also several technical and societal challenges associated with IoB that must be addressed for them to realize its potential. Some key challenges in this regard include the availability and bias of collected data, data-sharing incentives, privacy concerns, behavior extraction, explainability, behavior nudging strategies, and other ethical concerns. 

The need for transparency

Considering the ethical aspects of the IoB, businesses should make sure that customers’ data is gathered and analyzed transparently. Particular attention should be given to ensuring an individual’s privacy and informed consent before collecting and sharing data. Informed consent, which is the process of alerting and obtaining consent for data collection, analysis, and sharing, is another key element that needs to be carefully considered in IoB deployments. The interplay between incentives and consent should be well examined, as consent may lead to coercion in cases in which users must consent to receive certain services. Businesses should also ensure the implementation of adequate cybersecurity protection measures as sensitive behavioral data can be subject to misuse and malicious purposes. Another important aspect of the IoB is data validation, which emphasizes the need to assess the suitability of available data is for a specific application and the risks associated with poor data. 

Way forward

IoB applications can realize a wide variety of convenience and efficiency services to users and businesses. However, they come with a set of technical and societal challenges that need to be addressed for the IoB to earn the trust of individuals and businesses. These issues span the availability of useful data, to the appropriate design of intelligent techniques that extract behaviors from raw data, to the design of incentives for sharing data. Ethical issues that can hinder the ubiquity of their deployment need to be taken into consideration, especially as their presence becomes inexorable.