FRONT END INTELLIGENCE FOR

LARGE-SCALE APPLICATION-

ORIENTED INTERNET OF

THINGSPresented by:

Judy T RajCS7

ROLL NO:29

CONTENTS

• INTRODUCTION• TRADITIONAL PARADIGM• NEED FOR FRONT-END INTELLIGENCE• BANDWIDTH DEMAND & DELAY INTOLERANCE• DEVICE HETEROGENEITY

CONTENTS

• MOTIVATION FOR FRONT-END INTELLIGENCE• QUALITATIVE RATIONALE• ANALYTICAL RATIONALE• COMPARATIVE SIMULATION ANALYSIS

• INTELLIGENT IOT DEVICES: CONNECTIVITY PERSPECTIVE• CELLULAR NETWORK CONSIDERATIONS• SPECTRUM MANAGEMENT• D2D COMMUNICATIONS• MULTIHOP NETWORKING

• INTELLIGENT IOT DEVICES: APPLICATION PERSPECTIVE• INTELLIGENT IOT DEVICES: COLLABORATION PERSPECTIVE• SOFTWARE DEVICE DRIVEN ARCHITECTURE• CONCLUSION

INTRODUCTION• What is IoT?• Massive integration of electronic devices and other objects to collect &

exchange data.• Enabling technology for multitude of applications in healthcare,

wearables, surveillance, home automation etc• Aim of the paper

• Analyze the key performance matrices of back-end intelligence• Advocate empowerment of front-end intelligence in IoT• Discuss issues and challenges in empowering front-end IoT devices• Analyze from three perspectives of IoT design : connectivity,

collaboration and application.

TRADITIONAL PARADIGM

• Centralized architecture • Heavily reliant on the back-end core for all decision-making

processes• All operational and management decisions related to front-end

resources are made at the back-end.• Back-end or service platform in IoT is usually a cloud platform• Inefficient in terms of latency, network traffic management,

computational capacity i.e. Instructions processed per unit time and power consumption.

NEED FOR FRONT-END INTELLIGENCE

• This section outlines the design and operational challenges brought forward by two trends associated with the massive roll-out of IoT applications namely:• Bandwidth Demand & Delay Intolerance• Device Heterogeneity.

NEED FOR FRONT-END INTELLIGENCE• BANDWIDTH DEMAND & DELAY

INTOLERANCE

• Growing trend of bandwidth hungry or delay intolerant applications.

• Often observed in applications demanding real-time transmission of video streams.

• Examples include crowd management applications, industrial processes, geo-physical data acquisitions, cyber physical systems etc.

NEED FOR FRONT-END INTELLIGENCE

• DEVICE HETEREOGENITY• Refers to the wide spectrum of radio access technologies, network protocols,

capabilities, process power & communication technologies IoT devices support.

• No current standard for access protocols or messaging technologies for IoT.

• A Standardized homogenous solution is highly unlikely in future as well.

• Thus the need to cater to inherent technological diversity.

• Another disparity IoT gives rise to is multiple Administrative Domains (ADs).

• An AD refers to all IoT assets including front-end devices and back end platform under the jurisdiction of a single entity.

• The coexistence of multiple ADs need to be accounted for.

MOTIVATION FOR FRONT-END INTELLIGENCE• QUALITATIVE RATIONALE

• Interaction with other ADs cannot be performed if front-end devices are not entitled to execute basic data gathering operations about their neighbourhood of devices.

• The adverse implications of a highly-centralized paradigm can be mainly assessed from two distinct perspectives: networking efficiency and computational efficiency

• Advent of low-cost computationally efficient processing units like microcontrollers provide substantial motivation for front-end intelligent devices.

• QUALITATIVE RATIONALE• Five key performance matrices can analyze the performance of

IoT devices.• For back end intelligent systems, the corresponding values for

each term are:• Energy: higher energy consumption• Latency: longer trip times• Throughput: lower throughput• Scalability: Lesser scalability• Reliability: Lower reliability

MOTIVATION FOR FRONT-END INTELLIGENCE

MOTIVATION FOR FRONT-END INTELLIGENCE•ANALYTICAL RATIONALE • For large-scale multihop IoT setup where information must be exchanged for every

actuation.

• This cause lags and delays especially for FoT

MOTIVATION FOR FRONT-END INTELLIGENCE

• ANALYTICAL RATIONALE• Back-end centric paradigm: the information collected from nearby

neighbours are forwarded to the back-end core to make the right decision which is, in turn, sent to actuator to perform the required action.

• Front-end empowered paradigm, the device is supported with intelligence to take in-situ the decision based on the information collected from nearby sensors that directly communicate with the smart device.

MOTIVATION FOR FRONT-END INTELLIGENCE

• COMPARATIVE SIMULATION ANALYSIS• Paper investigated the impact of front end intelligence radio access procedure in LTE

networks and compared between three scenarios depending on the level of collaboration.

• A Radio Access Technology or (RAT) is the underlying physical connection method for a radio based communication network. As of 2013, many modern phones such as the Nexus 4 or iPhone5 support several RATs in one device such as Bluetooth, Wi-Fi, and 3G, 4G or LTE.

• Long-Term Evolution (LTE) is a standard for high-speed wireless communication for mobile phones and data terminals. It is based on the GSM/EDGE and UMTS/HSPA network technologies, increasing the capacity and speed using a different radio interface together with core network improvement

MOTIVATION FOR FRONT-END INTELLIGENCE• COMPARATIVE SIMULATION ANALYSIS

• Trad:all devices establish their connections with the node and complete their data transfer independently.

• Collab: devices belonging to the same AD collaborate together to achieve data transfer.

• Social: devices socialize together and act as if they belong to a single virtual AD.

MOTIVATION FOR FRONT-END INTELLIGENCE• DESIGN IMPERATIVE

• The paper aims to design a framework for the design of IoT devices to empower front-end intelligence.

• Can be achieved by viewing from three different perspectives.

• Proposed framework supports diversity in networking protocols.

• Comprehensive framework for design & optimization of wide range of applications.

• Advocates socialization schemes enabling devices of different ADs to share resources.

INTELLIGENT IOT DEVICES: CONNECTIVITY PERSPECTIVE

• Deals with main connectivity challenges i.e. Issues with cellular networks and spectrum management and major promising directions that could cope with these issues.

CELLULAR NETWORK CONSIDERATIONS

• Based on simulation and analysis of actual traffic patterns of LTE Release 10 UL air interface in Saudi Arabia, the paper concluded that a major capacity deficit occurs as applications become bandwidth hungry.• As the number of connections/frame arrival increases, resource allocation becomes less efficient and physical random access

channel becomes congested in network entry procedure.

• Drop rate and channel access waiting time increase as connections increase.

SPECTRUM MANAGEMENT

• Effective power control strategies to maintain net interference levels within operationally acceptable ranges need to be developed as the authors envision IoT communications outside licensed spectrum.

• PROMISING DIRECTIONS• Further advancements in D2D communications and Multihop

networking to cope with the issues in connectivity.• D2D communications: Is a technology component for LTE.

Enables direct communication between nearby mobile equipments facilitate interoperability.

• Multihop Networks: use two or more wireless hops to convey information from a source to a destination. Nodes in between source and destination communicate using wireless channels.

INTELLIGENT IOT DEVICES: CONNECTIVITY PERSPECTIVE

D2D COMMUNICATION

• Can boost capacity in terms of through put and number of connections.• Can be achieved by

ubiquitously available technologies like WiFi with effective scheduling to manage human made and M2M traffic.• Access scheduling can cause

extensive interference in D2D layer, battery depletion and BW accounting and billing challenges

MULTIHOP NETWORKING• Another method to circumvent capacity deficit issues.• Eg: smart grid, vehicle to vehicle communication• Advantages: Standardized protocols such as THREAD are available• Disadvantage: As the network builds in scale, protocol overhead occurs.

• Protocol overhead : metadata and network routing information sent by an application, which uses a portion of the available bandwidth of a communications protocol.

• Solved to some extent by enabling IoT devices to make relaying decisions locally .• IoT devices at a given hop extract position information encoded by the nodes of the previous

hop, and accordingly are able to locally make their routing decisions.

INTELLIGENT IOT DEVICES: CONNECTIVITY PERSPECTIVE

INTELLIGENT IOT DEVICES: APPLICATION ORIENTED PERSPECTIVE

• For optimal quality of service at end user level, few operational challenges exist:• Battery management• Device maintenance• Software upgrades

• QoS requirements for each application might be different and affects the design.• Paper recommends surveys to determine requirements before designing.

• Eg: Industrial locale has low throughput demand but is delay intolerant.

• Surveillance systems has high delay intolerance and bandwidth hunger.

• A descriptive framework to capture interactions between five KPMs for different applications.

INTELLIGENT IOT DEVICES: COLLABORATIVE & SOCIAL PERSPECTIVE

• Collaboration across ADs can meet the operational-application tradeoffs.• Existing IoT applications using multiple ADs work in silos.• Paper proposes a collaboration between different ADs to deliver best EUX within

constraints like QoS, privacy, security etc.• Collaboration in IoT is mostly in three planes:• Relaying & Routing information towards back-end server• Intelligence and sensor measurements• Computational resources

• Challenges: Different AD s have different QoS mandates• Proposed solution: A Virtual marketplace for devices in different Ads to trade

resources

SOFTWARE DRIVEN DEVICE ARCHITECTURE• Effectively integrates robust and communication platforms with provision

for different classes of traffic.• Devising network parameters at device level rather than infrastructure level

helps cater to device heterogeneity.• Objective is to develop software tools enabling front-end intelligence

combined with advantages of SDA like:• Socialization• Collaboration• Dynamic return of operational parameters

IMPLEMENTATION •End User Abstraction layer provides a GUI for users to provide inputs for customization of IoT device, translated to QoS.•Parameterization Engine intelligently maps QoS requirements to the best operational profile i.e outputs a set of optimal values for the device to observe.

CONCLUSIONThe main contributions of the paper are summarized as follows:

• Demonstrates the advantage of front-end intelligence for large-scale application-oriented IoT systems.

• Provides rather a novel model for optimizing the performance of application-oriented IoT in light of connectivity constraints and collaboration potential.

• Identifies non-debatable shortcomings of some of the existing techniques and technologies in the context making IoT devices intelligent.

• Proposes potential viable solutions while outlining open research directions.

In summary, this study defines a conceptual framework for future IoT devices enabling multiple innovative features for the IoT platform administrator as well as the end user. These smart IoT devices will have significant positive impacts on different domains allowing fast, reliable, and intelligent management of diverse IoT-based applications.

Thank you!

QUESTIONS?