Three to five years
Software Defined Radios
Software Defined Radio (SDR) is a software radio algorithm that puts most of the radio frequency (RF) into the digital domain, allowing for great flexibility in the modes of radio operation. Traditional radios are hard-wired to communicate using one specific protocol, e.g., mobile phones will need multiple radios to handle a variety of communication modes with cell phone towers, WiFi base stations and GPS signals. SDR works with raw electromagnetic signals and enables multiple radio functionalities with the use of software. This makes it possible for SDR devices to tune into many different frequencies simultaneously and make multiple communications with other devices which use specific radio protocols.
With billions of connected devices on current communication technologies, new communication needs will require faster connectivity and the frequency spectrum will have to be adapted to the new bandwidth requirements. Using SDR, the need to implement hardware upgrades when new protocols emerge can be removed. SDR will eventually find its way into mobile devices to enable them to operate across many radio bands and using many radio protocols and modulation techniques.
The technologies could become commercial in the next three to five years and will have a broad impact on IOT, especially M2M devices, so as to enable human-oriented devices to search for the best frequency, depending on a predetermined set of parameters. Currently one of the earliest examples of a commercial SDR is the range of ZTE 3G Dualcarrier High Speed Packet Access (DC-HSPA)/LTE universal radio-base stations that is deployed at CSL Hong Kong for the operator's new Frequency Division Duplex (FDD)-LTE service, launched in Q1 2012.
Less than three years and more than five years
LTE & LTE-A
LTE is a 4G wireless broadband technology developed by the Third Generation Partnership Project (3GPP), an industry trade group. 3GPP engineers named the technology "Long Term Evolution" because it represents the next step in a progression from GSM, a 2G standard, to UMTS, the 3G technologies based upon GSM. LTE provides significantly increased peak data rates, with the potential for 100 Mbps downstream and 50 Mbps upstream, reduced latency and scalable bandwidth capacity. Future developments could yield peak throughput to the order of 300 Mbps.
LTE-A is a major enhancement of the LTE standard developed by 3GPP and has been approved by the International Telecommunication Union (ITU) as the 4th generation (4G) radio technologies system. LTE-A is backward compatible with LTE and uses the same frequency bands while LTE is not backward-compatible with 3G systems. LTE-A has the potential for even faster peak data rates, with the potential for 1 Gbps downstream and 500 Mbps upstream.
With the increasing number of IOT sensors, M2M devices and various applications such as context-aware computing services generating huge amounts of real-time data, mobile data networks could experience millions of transactions or interactions between cloud servers, peer-to-peer communications and back-end systems. Leveraging on LTE and LTE-A networks which have been designed to increase network capacity, speed of mobile communications and low latency, IOT applications would enable substantial improvements in end user throughputs, application response times and user experience.
More than five years
Cognitive Networks
The definition of a cognitive network is a network that can perceive current network conditions to plan, decide, and respond, based on those conditions. The network can learn from these adaptations to make future decisions. Today’s networking technology limits a network’s ability to adapt, often resulting in sub-optimal performance. It is not designed for IOT as network usage tends to be more downlink than uplink intensive.
Network traffic is more IP based than hybrid e.g. Zigee and UWB and has a relative fixed access pattern compared to dynamic access pattern which includes multiple sensors, actuators, sensor gateways. Often being limited in scope and response mechanisms, the network elements are unable to make intelligent adaptations to suit networking requirements. Cognitive networks use observations of network performance as inputs to provide outputs in the form of a set of actions that can be implemented in the modifiable elements of the networks.
For example, in addressing network QoS, cognitive networks can utilise the feedback about observed network conditions to identify bottlenecks, change in resource prioritisation and optimising behaviour, to provide the desired end-to-end QoS. In another area such as network security, cognitive networks can react to security threats by analysing feedback from the various layers of the network to find patterns and risks, and to dynamically manipulate access control, trust management or intrusion detection to eliminate security threats. Some of the technologies needed in cognitive networks are software adaptable networks (SAN) and cognitive radio. SAN consists of the API, modifiable network elements and network status sensors.
It implements the actual network functionality and allows the cognitive process to adapt to the network. Cognitive radio is a transceiver that automatically changes its transmission or reception parameters. Its cognitive process has the capability to learn from past decisions and rely on observations, paired with knowledge of node capabilities, to influence future transmission behaviour. With various network requirements by IOT applications, the cognitive network will allow IOT devices and M2M machines to form a pervasive communication environment, designed to be extensible and flexible to ensure a certain level of QoS and user experience.
Software Defined Radios
Software Defined Radio (SDR) is a software radio algorithm that puts most of the radio frequency (RF) into the digital domain, allowing for great flexibility in the modes of radio operation. Traditional radios are hard-wired to communicate using one specific protocol, e.g., mobile phones will need multiple radios to handle a variety of communication modes with cell phone towers, WiFi base stations and GPS signals. SDR works with raw electromagnetic signals and enables multiple radio functionalities with the use of software. This makes it possible for SDR devices to tune into many different frequencies simultaneously and make multiple communications with other devices which use specific radio protocols.
With billions of connected devices on current communication technologies, new communication needs will require faster connectivity and the frequency spectrum will have to be adapted to the new bandwidth requirements. Using SDR, the need to implement hardware upgrades when new protocols emerge can be removed. SDR will eventually find its way into mobile devices to enable them to operate across many radio bands and using many radio protocols and modulation techniques.
The technologies could become commercial in the next three to five years and will have a broad impact on IOT, especially M2M devices, so as to enable human-oriented devices to search for the best frequency, depending on a predetermined set of parameters. Currently one of the earliest examples of a commercial SDR is the range of ZTE 3G Dualcarrier High Speed Packet Access (DC-HSPA)/LTE universal radio-base stations that is deployed at CSL Hong Kong for the operator's new Frequency Division Duplex (FDD)-LTE service, launched in Q1 2012.
Less than three years and more than five years
LTE & LTE-A
LTE is a 4G wireless broadband technology developed by the Third Generation Partnership Project (3GPP), an industry trade group. 3GPP engineers named the technology "Long Term Evolution" because it represents the next step in a progression from GSM, a 2G standard, to UMTS, the 3G technologies based upon GSM. LTE provides significantly increased peak data rates, with the potential for 100 Mbps downstream and 50 Mbps upstream, reduced latency and scalable bandwidth capacity. Future developments could yield peak throughput to the order of 300 Mbps.
LTE-A is a major enhancement of the LTE standard developed by 3GPP and has been approved by the International Telecommunication Union (ITU) as the 4th generation (4G) radio technologies system. LTE-A is backward compatible with LTE and uses the same frequency bands while LTE is not backward-compatible with 3G systems. LTE-A has the potential for even faster peak data rates, with the potential for 1 Gbps downstream and 500 Mbps upstream.
With the increasing number of IOT sensors, M2M devices and various applications such as context-aware computing services generating huge amounts of real-time data, mobile data networks could experience millions of transactions or interactions between cloud servers, peer-to-peer communications and back-end systems. Leveraging on LTE and LTE-A networks which have been designed to increase network capacity, speed of mobile communications and low latency, IOT applications would enable substantial improvements in end user throughputs, application response times and user experience.
More than five years
Cognitive Networks
The definition of a cognitive network is a network that can perceive current network conditions to plan, decide, and respond, based on those conditions. The network can learn from these adaptations to make future decisions. Today’s networking technology limits a network’s ability to adapt, often resulting in sub-optimal performance. It is not designed for IOT as network usage tends to be more downlink than uplink intensive.
Network traffic is more IP based than hybrid e.g. Zigee and UWB and has a relative fixed access pattern compared to dynamic access pattern which includes multiple sensors, actuators, sensor gateways. Often being limited in scope and response mechanisms, the network elements are unable to make intelligent adaptations to suit networking requirements. Cognitive networks use observations of network performance as inputs to provide outputs in the form of a set of actions that can be implemented in the modifiable elements of the networks.
For example, in addressing network QoS, cognitive networks can utilise the feedback about observed network conditions to identify bottlenecks, change in resource prioritisation and optimising behaviour, to provide the desired end-to-end QoS. In another area such as network security, cognitive networks can react to security threats by analysing feedback from the various layers of the network to find patterns and risks, and to dynamically manipulate access control, trust management or intrusion detection to eliminate security threats. Some of the technologies needed in cognitive networks are software adaptable networks (SAN) and cognitive radio. SAN consists of the API, modifiable network elements and network status sensors.
It implements the actual network functionality and allows the cognitive process to adapt to the network. Cognitive radio is a transceiver that automatically changes its transmission or reception parameters. Its cognitive process has the capability to learn from past decisions and rely on observations, paired with knowledge of node capabilities, to influence future transmission behaviour. With various network requirements by IOT applications, the cognitive network will allow IOT devices and M2M machines to form a pervasive communication environment, designed to be extensible and flexible to ensure a certain level of QoS and user experience.
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