Reliable interconnection between smart devices is the backbone of internet of things (IoT). Precise localization technique for ascertaining the position of these devices is thus imperative. GPS satellites and wireless technologies fail in variety of situations that interfere these signals. Notable conditions include places housing several reflecting surfaces and typical snags such as urban canyons. Numerous teams of researchers from different universities has leveraged the concept they call ‘localization of things’. The concept houses a system that has potential to offer localization information even reliably even in GPS-denied areas.
Machine Learning Techniques Improves Accuracy of System
The teams demonstrated the system worked closely to meet the theoretical limit of localization accuracy. It does so by leveraging what they call soft information for nodes in the network; nodes represent devices in IoT. The system extracts a wide array of features from the signals, notably including all possible contextual data. However, the vast data can be increasingly complex. Hence, researchers contended that for the system to be successful, they need to tone down the dimensions of these data. They achieved this by principal component analysis.
Another novel element to the system was incorporating machine learning (ML) techniques in the system. This equips the system to get acquainted with a statistical model that houses contextual data and measurements. The technique helps to reduce the effect of signal bouncing on the system.
Smart Cities to Harness The Potential
Industry players will harness the system in localization of things concept for various applications. Vast potential of the system serve as strong case for the expansion of the localization of things market. According to industry experts, the market will reach worth of no less than $128 billion by the end of 2027. Key ones are supply chain monitoring and autonomous navigation. In the coming years, connected smart cities will see the capabilities in connected smart cities in developed worlds. Further research will take place with nodes that have constraints of resources. The focus of the researchers will be to use as less computation power as possible.