Smart City with Internet of Things (Sensor networks) and Big Data

By – Satish Phakade Pawar – Professor, ASM’S IBMR, Chichwad, Pune (9860027825)

Keywords Internet of Things (IoT), Home Automation, Building Management Systems, Smart City, Sensor network, Big Data, 6LoWPAN, Cloud computing

Introduction From an Investor perspective a most attractive city has the best availability, manageability and performance ofTransport, water, energy, communication and buildings for residence work, entertainment and play. Internet of people with PC and Mobile devices is extending to Internet of Things (IoT).The vision of the internet of things is to manage objects around us with their own unique IP address. IoT will comprise of billions of devices that can sense, communicate, compute and potentially actuate. Traditional Approach to Automation of Home and Building Management Systems consists of disparate system such as Access control, Fire Alarm, Digital Surveillance, motion and presence detection, HVAC and energy management, sprinkler irrigation system, entertainment devices etc. These are, sometimes partially integrated by IP gateways but operate with proprietary protocols and standards (KNX, DALI, Modbus, Zwave, Zigbee). Recent emergence of 6LowPAN, a standard for low power wireless networks would enable networks of sensors and actuators natively to Internet Protocol V6. For an individual owner it could offer ability to address monitor and manage assets, with internet protocol better than ever with PC and mobile devices, e.g. a smart manageable light bulb. Collectively such IP based sensor networks could offer even more sense for a campus, neighbourhood, town or city for public utilities such as traffic, energy, water, communication and emergency management. Billions of Data streams coming from these largescale sensor networks will challenge the traditional approaches to data management and related context capturing techniques. Even after enabling cloud integration for storing, archiving and processing of IoT data, it would be difficult convert it into user community centric useful intelligence. Cloud based Big Data techniques offer some promise. Big Data in information technology is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, storage, search, sharing, analysis, and visualization. With such data high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization. In the cloud infrastructure, tools such as the Apache Hadoop Big Data Platform could be helpful for making sense.

In this paper, I explore a potential framework of integrating internet of things (sensors) to the Big Data model of computing in a smart city environment. Challenges in enabling this framework are discussed. It leads to a proposal for simplification of the integration with cloud based community centric applications with discussion of related issues and potential solutions.

Why a Smart city?

The trend of urbanization is growing word wide. More than 6.3B people, 60% of the population will be living in cities by 2050 (source: UN World Urbanization Perspective, the 2011 revision).

Every city is a complex ecosystem with a lot of subsystems to make it work such as work, food, cloths, residence, offices, entertainment, transport, water, energy etc. With the growth there is more chaos and most decisions are politicised, there are no common standards and data is overwhelming. The intelligence is sometimes digital, often analogue, and almost inevitably human. Caragliu [1] et al on study titled “ Smart Cities in Europe” conclude “ We find that the presence of a creative class, the quality of and dedicated attention to the urban environment, the level of education, multimodal accessibility, and the use of ICTs for public administration are all positively correlated with urban wealth Why use Internet of Things for enabling the city infrastructure intelligence? Obviously more benefits could be there, when objects can both sense the environment and communicate, they become tools for understanding complexity and responding to it swiftly laying the foundation for Smart City. In what’s called the Internet of Things, sensors and actuators embedded in physical objects—from roadways to thermostats—are linked through networks, using the same Internet Protocol (IP) that connects the Internet. These networks churn out huge volumes of data ready for analysis. IoT is one of Gartner’s top 10 technology trends for 2013, and was the focus of the CES 2013. What is the Internet of Things in practice?[2] Maybe the simplest definition is that the Internetof Things encompasses all the embedded devices and networks that are natively IP-enabled and Internet-connected, along with the Internet services monitoring and controlling those devices. Today’s Internet is made up of acore Internetof backbone routers and servers, massive no. of nodes infringe including PCs, mobiles and all other devices. The core Internet changes rarely and has extremely high capacity..In 2008 it was estimated that the Internet had approximately 1.4 billion regular users. The growth of the fringe is dependent on the number of Internet users and the personal devices used by them. The Internet of Things is the biggest challenge and opportunity for the Internet today. It is made up of the IP-enabled embedded devices connected to the Internet, including sensors, machines, active positioning tags, radio-frequency identification (RFID) readers and building automation equipment to name but a few. The exact size of the Internet of Things is hard to estimate, as its growth is tremendous. The Internet of Things will soon exceed the rest of the Internet in size (number of nodes) and will continue growing at a rapid rate in trillions of devices. The greatest growth potential in the future comes fromembedded, low-power, wireless devices and networks that until now have not been IP-enabled – theWireless Embedded Internet. In 2008 the IP Smart Objects (IPSO) Alliance [IPSO] was formed by industry leaders to promote the use of Internet protocols by smart objects and the Internet of Things through marketing, education and interoperability.The Wireless Embedded Internet is a subset of the Internet of Things; We define the Wireless Embedded Internet to include resource-limited embedded devices, often battery powered, connected by low-power, low-bandwidth wirelessnetworks to the Internet. 6LoWPAN was developed to enable theWireless Embedded Internetby simplifying IPv6 functionality, defining very compact header formats and taking the nature of wireless networks into account 6LoWPAN].

A Smart city enabled with IoT sensor networks would benefit the citizen in various ways -

i. Increasing Relevant Use of the Resources with Internet to Tell Us when and where to Save It’s estimated that major cities waste up to 50% of water due to leaky pipes. Irrigation systems run when it’s raining and street lights remain on even when there’s no activity in the area.Internet connected sensors can control detect unnecessary use and make adjustments. Irrigation systems can turn off when rain is detected, lights can go dim when they aren’t needed and leaking pipes can send a text to landlords. These improvements help us save resources by increasing efficiency and large amounts of money. ii. Systems that save Time, Energy and Money The Internet of Things can be used to empower the systems we’ve already created to work for us in the best way possible. For instance, public transportation could work even better with IoT and cloud based big data automation enabling operators and control centre to see their position in route, ticket sales, camera. With large displays and through mobile interfaces Bus riders could see real time positions and get notifications in time for the bus. It avoid time wastage at the stop or missing the bus. Revenue could be generated through location-based advertising.

iii. Improve Quality of life and Systems that save in emergency

For example, citizens can monitor the pollution concentration in each street of the city or they can get

automatic alarms when the radiation level rises a certain level. In case of emergency management

IV. Smart metering to monitor the optimum usage of energy , gas water etc.

B. Smart City based on IoT could benefit the authorities in various ways -

1. Energy management - To reduce energy wastage in street lighting of the city.

2. Water Management -Water leaks can be easily detected and corrected, Irrigation of parks can be

optimized

3. Transport management - Real time traffic maps can be obtained to enable smooth flow. Traffic can be

reduced with systems that detect where the nearest available parking slot is.[24] Motorists get timely

information so they can locate a free parking slot quickly, saving time and fuel. This information can

reduce traffic jams and pollution improves the quality of life.

An easier parking project [3] in San Francisco city at can be checked at www.sfpark.orgSFpark optimizes the use of existing parking resources to benefit drivers and everyone else. SFpark makes finding and paying for parking faster and easier. Access to real-time parking availability information online, via text, and through smartphone apps helps drivers find a space. Longer time limits and new meters that accept credit/debit cards, SFMTA parking cards and coins make parking more convenient and result in fewer parking tickets.In order to direct drivers towards parkingWireless parking sensors detect parking availability in real time. Metered parking spaces in SFpark pilot areas have wireless sensors embedded in the pavement.Parking sensors have been installed in 8,200 on-street spaces

4. Noise and Pollution management

5. Waste disposal management

6. Citizen Information system

7. Waste management -Rubbish bins can send an alarm when they are close to being full.

Global Trends for Smart City and IoT concepts [4] –

Investment in smart cities will grow from $8B to $16B by 2020 for a total of $108 billion between 2010 and 2020 (source http://www.pikeresearch.com/research/smart-cities).

By 2016, the IOT opportunity will be $23B, 20% of which is in Smart Buildings (source: 2011 Executive Yun SRB, Taiwan)

What are the questions that need to be answered for the concepts to deliver?

Connected devices are nothing without a service, so where is the service? A true internet of things experience isn’t in being able to connect devices to the web, but building a service based around that connectivity which is missing. (Which we propose to plug with cloud integration and Big data0 Where are the toll bridges in each ecosystem? Once you view the internet of things as many internets and think about standards for connectivity, software and even the clouds where data might be stored or processed, it becomes clear that each ecosystem will have different control points where it can make money. Sure, there’s money in selling the physical hardware , also services, data and online processing or storage that could also provide revenue opportunities Where will the intelligence in the internet of things live? will they communicate all the way back to a server in the cloud? Or will there be some local device that helps make decisions based on the information that the connected devices deliver? How many internets of things will we have? the vertical segmentation So while we might have an internet of electricity that combines elements of the smart grid with our thermostats, we may have to buy some kind of connector device that we plug our appliances into to get them connected to the internet of electricity. This year Velodyne helped Google create the first driverless car. This project demonstrated the car driving sans human from San Francisco to Las Vegas. The car followed the speed limit, abided by traffic laws, and completed its journey without any accidents. Car companies like Audi and Toyota have also released driverless models recently that are able to function without a physical driver.[5] If we have self-driving cars they will also need to connect to an internet of vehicles or transportation that will include other cars and traffic signals. How will internets of things from different equipment and cloud providers interoperate? Every day we deal with useful, sophisticated but standalone devices that have sensors and actuators. These devices are the appliances in the kitchen, the gadgets in living room, the lighting, heating, cooling, watering, draining facilities in the building, the thermometers, scales, and health meters, entertainment equipment, etc. They are stand-alone and fixed function. The challenge is to enable them to become a part of the sensor network for ease of use. Existing commercial Cloud services are proprietary in nature. They are owned and operated by individual companies. Each of them has created its own closed network, which is expensive to setup and maintain. Interoperability and mobility may be necessary for data storage and analysis with different cloud service providers. What about privacy? Sharing of data for sensors located in common areas would be a concern even for office complexes let alone residential places, for the fear of privacy breach.

Sensor Networks[6] A sensor network [ ] is a group of specialized transducers with a communications infrastructure intended to monitor and record conditions at diverse locations. Commonly monitored parameters are temperature, humidity, pressure, wind direction and speed, illumination intensity, vibration intensity, sound intensity, power-line voltage, chemical concentrations, pollutant levels and vital body functions. A sensor network consists of multiple detection stations called sensor nodes, each of which is small, lightweight and portable. Every sensor node is equipped with a transducer, microcomputer, transceiver and power source. The transducer generates electrical signals based on sensed physical effects and phenomena. The microcomputer processes and stores the sensor output. The transceiver, which can be hard-wired or wireless, receives commands from a central computer and transmits data to that computer. The power for each sensor node is derived from the electric utility or from a battery. Potential applications of sensor networks include: Industrial automation,Automated and smart homes,Video surveillance, Traffic monitoring, Medical device monitoring, Monitoring of weather conditions, Air traffic control, Robot control. Check www.ninjablocks.com for an example of easy to use sensors for motion, temperature, humidity , contacts and actuators with the Ninja Blocks platform makes it trivial to build web & mobile apps that talk to hardware. A smart grid sensor [7]is a small, lightweight node that serves as a detection station in a sensor network. Smart grid sensors enable the remote monitoring of equipment such as transformers and power lines and the demand-side management of resources on an energy smart grid. Smart grid sensors can be used to monitor weather conditions and power line temperature, which can then be used to calculate the line’s carrying capacity. This process is called dynamic line rating and it enables power companies to increase the power flow of existing transmission lines. Smart grid sensors can also be used within homes and businesses to increase energy efficiency. Sensor Networks have been traditionally deployed for various functions. A sensor network is a group of specialized transducers with a communications infrastructure intended to monitor and record conditions at diverse locations. likes Choosing a the best protocol for and mobility Software Architecture for Wireless Sensors A critical step towards achieving the vision behind wireless sensor networks is the design of a software architecture that bridges the gap between raw hardware capabilities and a complete system. The demands: It must be efficient in terms of memory, processor, and power. It must also be agile enough to allow multiple applications to simultaneously use system resources such as communication, computation and memory. The extreme constraints of these devices make it impractical to use legacy systems. TinyOS [8]is an operating designed explicitly for networked sensors. TinyOS draws strongly from previous architectural work on lightweight thread support and efficient network interfaces. Included in the TinyOS system architecture is an Active Messages communication system a fit with the event based nature of network sensor applications. TinyOS is designed to scale with the current technology trends supporting both smaller, tightly integrated designs, as well as the crossover of software components into hardware. communication. In order to enable the vision of single-chip, a low cost sensor node, TinyOS combines a highly efficient execution model, component model and communication mechanisms. Traditional Approach to Automation of Home and Building Management Systems consists of disparate system such as Access control, Fire Alarm, Digital Surveillance, motion and presence detection, HVAC and

energy management, sprinkler irrigation system, entertainment devices etc. These are, sometimes partially integrated by IP gateways but operate with proprietary protocols and standards (KNX, DALI, Modbus, IEEE 802.15.4 ,ZigBee, Wireless HART, KNX, SimpliciTI, WiMi, EnOcean, Dash 7, WISA, Z Waveetc). Choosing the best wireless protocol [9] Some network protocols like 6LoWPAN,Dash7, Insteon and WiFi, are designed to communicatealso with a wired protocol to extend range or build subnets to lower the utilisation of the channel. Only a few protocols are capable of handling more than a few thousand nodes either directly or via subnets. ZigBee, 6LoWPAN, EnOcean, Dash7, ONE-NET and Insteon can be used for building automation of bigger office buildings. The other protocols are designed for smaller networks like home automation. There is always a trade-off between data transfer rate, speed and power consumption..WiMax, WiFi (n and g) and IEEE 802.15.3a havea higher power consumption for a higher bandwidth com-pared to the wireless sensor networks and Bluetooth whichwere designed as low power networks. Depending on the environment the wireless net-works cant operate within full range with their full band-width, therefore most protocols support lower data rate to extend their range Why 6LoWPAN? A large number of applications using proprietarytechnology could benefit from a Wireless Embedded Inter-net approach. Proprietary technologies are difficult to integrate into larger networks and with Internet-based services. The benefits of using Internet protocols in these applications, and thus integrating those with the Internet of Things include [RFC4919]: • Easy connectivity to other IP networks without translation gateways or proxies. •Use of existing IP network infrastructure. •IP-is very well documented, understood and has proven to work and scale. •Thesocket API is widely used. •Tools for managing, commissioning and diagnosing IP-based networks already exist Many management protocols need optimization for direct use with 6LoW-PAN. Direct communication with traditional IP networks requires many Internet protocols, often requiring an operating system to deal with the complexity and maintainability. 6LoWPAN [9] 6LoWPAN is an acronym of IPv6 over Low power Wire-less Personal Area Networks. This protocol is designed to be used for energy management, home and building automation. This protocol operates only in the 2.4 GHz frequency range with 250 kbps transfer rate. There is no encryption defined by the 6LoWPAN protocol, the range is up to 200 meter and the maximum nodes are limited to 100 per

network. This limitation exists for one channel and can be extended using wired backbones to connect

subnets. Extending IP to low-power, wireless personal area networks (LoWPANs) Using ip was once considered impractical because these networks are highly constrained and must operate unattended for multiyear lifetimes on modest batteries. Many vendors embraced proprietary protocols, assuming that IP was too resource-intensive to be scaled down to operate on the microcontrollers and low-power wireless links used in LoWPAN settings. However, 6LoWPAN radically alters the calculation by introducing an adaptation layer that enables efficient IPv6 communication over IEEE 802.15.4 LoWPAN links.

Volume and velocity of sensor network data Real-time Big Data means the streaming, continuous integration of high volume, high velocity data from all sources to all destinations, coupled with powerful in-memory analytics. It’s a paradigm shift from conventional store and process systems. Take for instance Intelligent Transportation application - now encompassing telematics, wireless telecommunications and M2M, ,the issue is how to deliver continuous, real-time information when the existing legacy systems are unable to scale. This hasn’t been an issue in the past, but the huge increase in sensor and telematics data is starting to cause pain, even for the largest of organizations. Framework of Sensor – Cloudintegration[11]

Cloud computing has emerged as a growingcomputing trend to provide anywhere on demand services for storage, compute and database resources. The application vendor can make available the “software as a service” “Web 2.0” technologies . Consumers will be able to access applications and data from a “Cloud” anywhere in the world on demand. In other words, the role of the cloud is to provide a place where application “suppliers” can make apps available to clients. The applications are then hosted as “Software as a Service (SaaS)” and the Cloud automatically scale to meet client demand. Wireless sensor networks (WSNs) connected to Internet would causeexplosivegrowth and would require cloud technologies to store, analyse , mine and derive actionable intelligence from digital information. This information could enable novel applications for social networks or virtual communities, blogs, Our primary goal is to facilitate connecting sensors, people and software objects to build community-centric sensing applications where people can share and analyse real time sensor data. Cloud data centres from providers such as Amazon are designed to provide on-demand capacity for the application providers. The application providers want to be sure that the user never ‘sees’ the data centre, only the application service. Examples

1. In Healthcare, doctors monitoring patient healthcare sensor data of all types will require analysis and mining on-the-fly. The Internet of Things can collect information from our bodies, store historical data and let our doctors know when something is wrong. E.g.1.wireless EKG machines allow doctors to collect heart patient data remotely. 2. For Diabetics iBGStara blood glucose meter with connectivity to the iPhone allows to view and share accurate data, to share with doctors.

2. Roads have sensors and can communicate with the vehicles passing over them to determine traffic patterns, find more sustainable ways to route cars, and perhaps even generate data to be sold to insurance companies or other businesses seeking to tap transportation information. But there is a question of who owns the data and what what software talks to those sensors.

Other than ownership and interoperability questions, there's also the question of how long companies should store the data and who has access to it. With so many disparate sources of data, and no real vision right now for a way to get all the data formatted in a manner that could be used by any number of interested parties. However, the amount of data is only going to continue to rise, so figuring out how to manage it, what to keep, and how to mine it for useful information will become increasingly important. Effectively utilizing this data—from energy to fuel consumption to weather data—could also provide valuable tools for environmental sustainability. The integration of sensor network analytics with cloud based community applications with acceptable QoS would derive intelligence from the data. E.g. traffic patterns, rain and water logging, large event crowd management.

Crossover Point [12] Within the next five years, sensor data will hit the crossover point with unstructured data generated by social media. From there, the sensor data will dominate by factors 10 to 20 times that of social media. However, using this data will be difficult for the time being, as there are no standards to ensure the data's readability beyond those possessing the right software or algorithm. Big Data Application to unstructured data Big data is a term applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly

moving target currently ranging from a few dozen terabytes to many petabytes of data in a single data

set.(Source Wikipedia)

Unstructured data streams rapidly and constantly. This data is heterogeneous and variable in nature and comes in many formats, including text, document, image, video, and sensed data. These areuntapped sources of insight, and big data analytics can reveal important interrelationships that were previously difficult or impossible to determine. 'Big data' refers to a collection of tools, techniques and technologies which make it easy to work with data at any scale. These distributed, scalable tools provide flexible programming models to navigate and explore data of any shape and size, from a variety of sources. Amazon Web Services provides easy, secure low cost access to tools such as Hadoop and NoSQL data stores, reducing the friction of working with your data at any scale. NoSQL movement has fuelled the big data significantly. As storage is essential component in big data, there are numbers of commercial and open source solutions available that supports big data requirements. - Arkady Zaslavsky et al have discussed “Sensing as a Service and Big Data” *13+ In NoSQL technologies, there are four varieties: Key-value, document store, wide column stores, and graph databases. Out of these four technologies, graph databases are favoured to be used to store information with complex relationships among pieces of data such as social network data, semantic and linked data. Other three technologies are more favoured to be used commonly across in big data, cloud and IoT applications. Popular key-value based data storage products are Dynamo (aws.amazon.com/dynamodb), Redis (redis.io), Riak (basho.com/Riak), Amazon SimpleDB (aws.amazon.com/simpledb), and Windows Azure Table Storage (windowsazure.com). Popular document store technology based products are CouchDB(couchdb.apache.org), and MongoDB (mongodb.org). Popular wide column based products are Apache HBase(hbase.apache.org), Hadoop (hadoop.apache.org), and Cassandra (cassandra.apache.org). However, the one getting the most attention is Hadoop as it based on popular distributed processing friendly MapReducetechnology. However, thereis no single perfect data management solution for the cloud to manage big data Tools Example

1. Amazon Tools http://aws.amazon.com/Amazon EC2 and Amazon Elastic MapReduce Amazon Elastic Compute Cloud (EC2) provides resizable compute capacity in the cloud, Amazon Elastic MapReduce builds on top of Amazon EC2, making it easy to deploy high performance, elastic Hadoop clusters .Amazon DynamoDB is a fully managed NoSQL database service that

provides fast and predictable performance with seamless scalability. Users can create secure, integrated infrastructure with Amazon EC2 and Amazon Elastic MapReduce with the Virtual Private Cloud.

2. MicroStrain’sSensorCloud™ [14]is a unique sensor data storage, visualization and remote management platform that leverages powerful cloud computing technologies to provide excellent data scalability, rapid visualization, and user programmable analysis. Originally designed to support long-term deployments of MicroStrain wireless sensors, SensorCloud now supports any web-connected third party device, sensor, or sensor network through a simple OpenData API.

The user software features requirements from the service providers:

1. Data storage with redundant reliability for preserving long-term sensor data streams 2. Time series visualization & graphing tool with exceptionally fast response, allows viewers to

navigate through massive amounts of data, and quickly zero in on points of interest

3. Quickly develop and deploy data processing and analysis apps in the cloud 4. Flexible SMS and email alert scripting features 5. useful for Structural health monitoring and condition based monitoring of high value assets 6. data sharing and analysis for team members spanning multiple locations 7. Tools to Visualize , Analyse., Collaborate and scale

Conclusion- Smart city concept has a great potential improve the quality of life by use of Internet of Things paradigm. Deployment of Wireless Sensor Networks would provide huge amount of data on one hand but could add massive and unstructured data management and analysis challenges. Cloud based storage and Big Data techniques show promise to generate actionable intelligence from these data streams.

Reference

[1] Caragliu, A; Del Bo, C. &Nijkamp, P (2009). "Smart cities in Europe". Serie Research Memoranda 0048 (VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics). [2] 6LoWPAN: The Wireless Embedded Internet Zach Shelby and Carsten Bormannc 2009 John Wiley & Sons, Ltd

[3] Gascón, David; Asín, Alicia;Smart Sensor Parking Platform enables city motorists save time and fuel

[4] EnablingSmart System Services in Smart Cities - Dr.Ko-Yang Wang, CTO and Executive Vice President

Institute for Information Industry Taipei

[5}http://www.royaldeerdesign.com/blog,ces_2013_introducing_the_internet_of_things,528.html

[6] Sensor network definition from http://searchdatacenter.techtarget.com/definition/sensor-network [7] http://searchnetworking.techtarget.com/definition/smart-grid-sensor [8]http://www.tinyos.net/ [9] Choosing the best wireless protocol for typical applications Klaus Gravogl, Jan Haase, Christoph GrimmInstitute of Computer Technology , Vienna University of Technology, Austria [10]Extending IP to low-power, wireless personal area networks (LoWPANs) By Jonathan W. Hui of Arch Rock David E. Culler University of California, Berkeley [11] Framework of Sensor - Cloud Integration Opportunities and Challenges Mohammad Mehedi Hassan(hassan@khu.ac.kr ), Biao Song ,Eui-Nam Huh Dept. of Computer Engineering ,Kyung Hee University , South Korea

[12] http://www.businessweek.com/technology/content/sep2010/tc20100914_284956.htm

[13][Sensing as a Service and Big Data – Arkady Zaslavsky, , Charith Perera , Dimitrios Georgakopoulos

[14] http://www.sensorcloud.com/system-overview