IOT Challenges and Opportunities new

IOT Challenges and Opportunities

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Internet of Things


As Internet of Things continues to expand to tens of billions of devices, it is bound to face numerous challenges in security, infrastructure, business models as well as industry. As much as it is difficult to predict exactly how IoT will continue to evolve, just like the numerous networking and IT technologies, it is expected to encounter many barriers. However, these challenges can be viewed in a positive light as they can give rise to lucrative and effective opportunities. For these reasons, Internet of Things will have to counter these expected challenges that range from connectivity to security and privacy in order to gain ground in the highly competitive market. As such, this paper aims at analyzing the various challenges or obstacles as well as opportunities that are associated with Internet of Things.


High Costs

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).

High Number of Devices

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).

Speed and Accuracy

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.


One of the most important requirements that should be considered when implementing Internet of Things in terms of network, security and privacy is IEEE standardization (Al-Fuqaha et al., 2015). This is especially necessary regarding IPv6 packet as it routes through various heterogeneous networks. Due to the rapid growth of IoT, standardization has become difficult. However, the aspect of standardization plays a vital role in development as well as spread of IoT. Standardization therefore aims at lowering entry barriers which limit new users and service providers, improving interoperability and allowing products to compete better at higher levels.

Various standardizations which are used in IoT can be important enablers for the rapid spread of IoT technologies. As such, they need to be designed in such a way that they embrace emerging technologies. Specific challenges in IoT include interoperability, radio access level, privacy and security. Moreover, industry specific requirement are recommended to ensure easier integration of the different services.

Security and Privacy

Security is bound to be a major concern when networks are used in large scale (Al-Fuqaha et al., 2015). The reason is that the system can be attacked in many ways. Moreover, the three components of IoT are very vulnerable to attacks. Passive RFID is the most vulnerable since it enables person tracking and no intelligence can be embedded on such devices. To counter outside trackers, encryption can ensure that data confidentiality is attained. However, it doesn’t protect against many inside malicious attacks. Security in the Cloud is therefore an essential area of research that needs more attention. Together with the presence of tools and data, clouds handle economic of IoT that will make it a greater threat for attackers. Security as well as identity protection is critical in hybrid clouds since public and private clouds will be utilized by businesses (Da et al., 2014).


Connecting many devices is bound to be a major challenge in the future of IoT. It will defy underlying technologies and current communication models. Currently, connection relies on centralized and server/client paradigm which authenticates authorizes and connects different nodes in a network. This model will not be sufficient when the network grows into billions of devices. As such, centralized systems will be bound to run into bottlenecks (Al-Fuqaha et al., 2015).

Consequently, these systems will need large investments as well as spending so as to maintain cloud servers which can handle significant amounts of information exchange. The future will therefore depend on decentralization of IoT networks. This will be possible by moving various tasks to edge. Other solutions would involve the application of peer-to-peer communications. Here, devices authenticate and identify with each other and exchange information without involving brokers.


This refers to the ability of adding new services, functions and devices to customers without negatively affecting the existing quality of services. This can be a hard task due to communication protocols and diverse hardware platforms (Al-Fuqaha et al., 2015). A generic IoT architecture introduces three layers of IoT daemon which are featured with intelligence, automation and zero configuration. This guarantees interoperability and scalability in the IoT environment. In a bid to deliver scalable services, IoT-iCore aims at providing an altered framework which offers scalable mechanisms for discovery of entities, look-up and registration in objects.


Wireless Low Power Sensors

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.

Better Connectivity

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.

Zero Touch Analysis and Management

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

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.


Today, the biggest problem when developing generic smart home solutions is the expense related to integration of smart home devices. Interoperability is a solution to open markets in competitive solutions in IoT. As such, leading companies which produce various smart devices are aiming at attaining full interoperability which will ensure that there is easy integration with the internet. Z-wave products are interoperable with the past versions (Díaz et al., 2016). Zigbee has recently formed numerous committees that will define product properties which are required for various vendors to create interoperable devices for the various public application profiles such as Health Care and Home Automation. The large number of technologies and devices make interoperability a significant problem.

False Data

With the captured device or node in IoT, the adversary can easily interchange normal data for false data. In turn, the false data can be transmitted to IoT applications (Lin et al., 2017). After receiving this false information, IoT devices return erroneous feedback or provide wrong services that affect the effectiveness and efficiency of networks. To avoid such malicious attacks, techniques that detect and drop the false data should be identified.

Big Data and Real Time Analysis

Internet of Things data usually comes in large amounts which varies in terms of structure, arrives at real time and is sometimes in uncertain provenance (Al-Fuqaha et al., 2015). As such, this makes the need for analytics and storing solutions that will provide vital insights to become very complex. Traditional SQL queried RDBMS are very suitable for the task and this is the reason why big data solutions are required. The IoT Cloud therefore enables complex analysis and longtime storage of this data. The challenge associated with handling big data is usually critical bec ause general performance is directly related with properties of the particular data management service (Stojkoska & Trivodaliev, 2017).

In this regard, a combination of tools has evolved with the aim of servicing the market, most specifically NoSQL databases as well as various business intelligence programs. There are many vendors which operate in various parts of the analytics pipeline (core analytics, data integration, data presentation and data storage) and “full stack vendors”. Both the open source and proprietary solutions usually adopt the alternative database technologies when handling big data: key-value, time-series, wide column stores, document stores as well as graph data bases. However, currently there isn’t a simple answer that solves the big data management problem in the Cloud especially when dealing with data integrity. This is because of the privacy and security related aspects when dealing with outsourced data.

Cloud Computing

Cloud computing and integrated IoT should be able to support large numbers of users and combine services that are offered by many stakeholders. As such, both need to operate in wireless and wired networks to deal with data sources and access devices that have unreliable connectivity and limited power. Cloud application platforms should support the many applications by availing domain specific environments and programming tools. Additionally, they should execute application harnessing capabilities of heterogeneous resources so as to meet quality requirements of the various users. Moreover, Cloud application should exhibit multi-object optimization as well as task duplication based default tolerance. It should provide synchronization, balancing, standardization, management, reliability as well as enhancement (Fuqaha et al., 2015).

IOT GIS Based Visualization

Since new display technologies continue to emerge, creative visualization needs to be enabled. The transition from CTR to Plasma and other displays has led to the rise of effective data representation that uses touch interface (Miraz et al., 2015). With new 3D displays, this particular area is bound to have extensive research as well as development opportunities. On the other hand, data which originates from ubiquitous computing isn’t ready for consumption especially using various visualization platforms and this requires further processing. This situation is especially complex in heterogeneous spatio-temporal data. As such, to cope with the various challenges, it is necessary to develop a framework that is based on internet GIS.


Regulation standards especially for data marketers are missing for data brokers. These are companies which sell data which is collected from various sources. As much as data appears to be currency for the IoT, there is a clear lack of transparency regarding who gets access to certain amount of data and exactly how this data is used to develop services and products which are then sold to third parties and advertisers (Miraz et al., 2015). As such, there is a great need for clearly stipulated guidelines regarding the use, retention as well as security of the data. This includes metadata, data which is described as other data.


In conclusion, Internet of Things is a new channel of technology advancement that is still in its early stages of market development. Just like many preceding waves that have been witnessed in technology evolution, Internet of Things is characterized by fragmentation, emerging standards, innovation, competitive jostling as well as confusion. Cumulatively, all these factors give rise to challenges as well as opportunities. As such, the new technology should aim at addressing these challenges which can in turn be converted into areas of research and study. Consequently, this provides a wide variety of opportunities that strengthen the new technology and enable it to gain stability in the market.


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