IOT Challenges and Opportunities

IOT Challenges and Opportunities

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IOT Challenges and Opportunities

The Internet of Things can be divided into four main layers. They include sensors which collect data, communication units which relay the collected information, computing units that analyze the information and lastly the service layers which eventually take action. For numerous sensors to be deployed, high costs will be incurred to service them adequately (Van & Bassi, 2012). As such, one of the main challenges is the lack of sensor technology which requires zero or even minimal effort to maintain and deploy. Some of these costs are incurred in battery replacement. The reason is that it is usually almost impossible to replace the batteries once they are in the field. For this reason, low power sensor design is another challenge to the IOT (Xu et al., 2014).

Connecting the increasing number of devices is yet another challenge. Today, the numbers of devices that are connected to the internet are more than the number of humans. In fact, this number is expected to increase in the future. Moreover, many of the sensors are expected to be connected to cellular networks, Wi-Fi and Bluetooth (Zhou, 2017). This therefore poses as a challenge since base stations are set up in a way that they provide quality service to a limited number of users. As such, with an increase in the number of users, some users are bound to receive compromised service. Based on the fact that the devices will be ordered in larger numbers than the quantity of human users, the problem will become even more serious (Gazis et al., 2015).

Moreover, generating timely and accurate answers is a huge challenge (Lee & Lee, 2015). In fact, various analytical approaches are based on the assumption that all data is available on the server. However, it requires both bandwidth and power to communicate this data to the servers. As the devices become more powerful, there will be a requirement for intelligent computation which must be distributed efficiently across both the cloud and the devices. Moreover, machines are meant to work for people. However, when performing an engine search for example, most of the work is done by the user instead of the machine (Chen et al., 2014). Consequently, the machines provide a list of results prompting the user to then select one of them. Through this process, the machines refine the user’s future search results. However, one of the challenges is getting the machine to perform these duties instead of the user.

Despite all these challenges, Internet of Things still has numerous opportunities to explore. These opportunities are mainly research and development in nature (Chiang et al., 2016). One of the opportunities is the development of low power wireless sensors. This is due to the fact that it is already a challenge to design some low-power sensors that don’t require battery replacements. This therefore creates a consequent demand for designs that are energy-efficient. More specifically, accurate sensor modules that are highly accurate usually consume high amounts of power. As such, an opportunity can arise with the development of low-accuracy modules that have low power consumption. Moreover, designing of new video processing and encoding algorithm is a great opportunity for IOT. The reason is that traditional video encoders are more complex than video decoders.

Another opportunity lies in the development of better connectivity (Biswas et al., 2014). The fact that many wireless standards are not able to support a significant number of devices is a problem that provides an opportunity for exploration. As such, one of the approaches can be formation of clusters of machines. Instead of directly communicating with the base stations, machines can talk to nearby cluster heads which consequently pass the information to the base station. In turn, this will reduce the power demanded during machine transmission and increase the spatial reuse of the spectrum.

The “Zero Touch” management and analysis provides an ideal opportunity. More specifically, there is a need to develop a scalable, robust, optimized and flexible application framework for data security, data analytics as well as sensor management (Valdivieso et al., 2014). Moreover, turning data into useful wisdom or contexts is extremely critical. As such, another great opportunity lies in the design of decentralized, context-aware analysis algorithm. In this regard, devices are bound to be computationally capable which means that intelligent computation can be easily disturbed between backend servers and sensors. As such this technology provides an opportunity for computation to easily migrate from one hardware piece to another.

Smart service for people is another opportunity that is present for IOT (Biswas et al., 2014). The diverse and fragment standards as well as interfaces that lie between the various layers of the system usually hinder innovation capabilities of the application developers as well as service providers. If the industry is to grow as powerfully and reliably as Moore’s Law, there is an urgent need to develop a similar type of mutually advantageous and independent industry structure that makes the PC hardware and software industry spiral to function. In this regard, standard interfaces are the source of the spiral. If all individual components providers have fixed boundary conditions, it becomes easier to create innovative solutions.

In conclusion, as numerous devices are added to the internet, Internet of Things will transform the way people play, live and work. As an exciting area of innovation, IOT offers various challenges as well as opportunities. The challenges include the high cost of servicing sensors, sensor technology which requires minimal effort to deploy and low power sensor design. On the other hand, the opportunities include better connectivity, zero touch analysis and management, low power wireless sensors and smart service for people.

References

Biswas, A. R., & Giaffreda, R. (2014, March). IoT and cloud convergence: Opportunities and challenges. In Internet of Things (WF-IoT), 2014 IEEE World Forum on (pp. 375-376). IEEE.

Chen, S., Xu, H., Liu, D., Hu, B., & Wang, H. (2014). A vision of IoT: Applications, challenges, and opportunities with china perspective. IEEE Internet of Things journal, 1(4), 349-359.

Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of Things Journal, 3(6), 854-864.

Gazis, V., Goertz, M., Huber, M., Leonardi, A., Mathioudakis, K., Wiesmaier, A., & Zeiger, F. (2015, February). Short paper: IoT: Challenges, projects, architectures. In Intelligence in Next Generation Networks (ICIN), 2015 18th International Conference on (pp. 145-147). IEEE.

Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440.

Valdivieso Caraguay, A. L., Benito Peral, A., Barona Lopez, L. I., & Garcia Villalba, L. J. (2014). SDN: Evolution and opportunities in the development IoT applications. International Journal of Distributed Sensor Networks, 10(5), 735142.

Van Kranenburg, R., & Bassi, A. (2012). IoT challenges. Communications in Mobile Computing, 1(1), 9.

Xu, T., Wendt, J. B., & Potkonjak, M. (2014, November). Security of IoT systems: Design challenges and opportunities. In Proceedings of the 2014 IEEE/ACM International Conference on Computer-Aided Design (pp. 417-423). IEEE Press.

Zhou, J., Cao, Z., Dong, X., & Vasilakos, A. V. (2017). Security and privacy for cloud-based IoT: Challenges. IEEE Communications Magazine, 55(1), 26-33.