Sensor Networks - Part III PDF

EL Sensor Networks– Part III PT Dr. Sudip Misra Associate Professor Department of Computer Science and Engineering IIT KHARAGPUR N Email: [email protected] Website: http://cse.iitkgp.ac.in/~smisra/...

EL Sensor Networks– Part III PT Dr. Sudip Misra Associate Professor Department of Computer Science and Engineering IIT KHARAGPUR N Email: [email protected] Website: http://cse.iitkgp.ac.in/~smisra/ Introduction to Internet of Things 1  Target Tracking EL Fig a: Push‐based formulation: Nodes compute the position of the target and periodically notify the sink node. A cluster structure is commonly used in this case PT Fig b: Poll‐based formulation: Nodes register the presence of the target to permit a low‐cost query. Data reports are sent toward the sink only when Fig c: Guided formulation: Some nodes (beacon nodes) define a trajectory to the target. The tracker follows this trail to N there is a query to be answered. Tree structure is intercept the target. Face structure is often often used in this case used in this case Source: Éfren L. Souza, Eduardo F. Nakamura, and Richard W. Pazzi. 2016. Target Tracking for Sensor Networks: A Survey. ACM Computing Survey, 49, 2, 2016 Introduction to Internet of Things 2  Target Tracking (contd.) EL PT N Source: Éfren L. Souza, Eduardo F. Nakamura, and Richard W. Pazzi. 2016. Target Tracking for Sensor Networks: A Survey. ACM Computing Survey, 49, 2, 2016 Introduction to Internet of Things 3  WSNs in Agriculture  AID: A Prototype for Agricultural Intrusion Detection Using Wireless EL Sensor Network  A set of sensor nodes are deployed over an agricultural field  Each of the board are enabled with two type of sensors: a) Passive Infrared (PIR) b) Ultrasonic PT  When an intruder enters into the field through the boundary (perimeter) of the field, the PIR sensor detects the object. N  The ultrasonic sensor senses the distance at which the object is located Source: Sanku Kumar Roy, Arijit Roy, Sudip Misra, Narendra S Raghuwanshi, Mohammad S Obaidat, AID: A Prototype for Agricultural Intrusion Detection Using Wireless Sensor Network, IEEE International Conference on Communications (ICC), 2015 Introduction to Internet of Things 4  WSNs in Agriculture (contd.) EL PT N Source: Sanku Kumar Roy, Arijit Roy, Sudip Misra, Narendra S Raghuwanshi, Mohammad S Obaidat, AID: A Prototype for Agricultural Intrusion Detection Using Wireless Sensor Network, IEEE International Conference on Communications (ICC), 2015 Introduction to Internet of Things 5  Wireless Multimedia Sensor Networks (WMSNs)  Incorporation of low cost camera (typically CMOS ) to wireless sensor nodes EL  Camera sensor (CS) nodes  capture multimedia (video, audio, and the scalar) data, expensive and resource hungry, directional sensing range  Scalar sensor (SS) nodes PT  sense scalar data (temperature, light, vibration, and so on), omni‐ directional sensing range , and low cost  WMSNs consist of less number of CS nodes and large number of SS nodes N Source: S. Misra, G. Mali, A. Mondal, "Distributed Topology Management for Wireless Multimedia Sensor Networks: Exploiting Connectivity and Cooperation", International Journal of Communication Systems (Wiley), 2014 Introduction to Internet of Things 6  Wireless Multimedia Sensor Networks (WMSNs)  WMSNs Application EL  In security surveillance, wild‐habitat monitoring, environmental monitoring, SS nodes cannot provide precise information  CS nodes replace SS nodes to get precise information PT  Deployment of both CS and SS nodes can provide better sensing and prolong network lifetime N Source: S. Misra, G. Mali, A. Mondal, "Distributed Topology Management for Wireless Multimedia Sensor Networks: Exploiting Connectivity and Cooperation", International Journal of Communication Systems (Wiley), 2014 Introduction to Internet of Things 7  Topology Management in WMSNs Video data are larger in size (e.g., 1024 bytes) which require larger bandwidth and consume high battery power EL Coverage of the event should be provided as soon as the event occurs Connectivity is another important metric that should be provided during video data transfer from the event area to the control center PT Therefore, Misra et al. proposed the distributed topology management of the WMSNs considering coverage, connectivity, and network lifetime Coverage of the event is provided by using Coalition Formation Game between the CS and SS nodes N Source: S. Misra, G. Mali, A. Mondal, "Distributed Topology Management for Wireless Multimedia Sensor Networks: Exploiting Connectivity and Cooperation", International Journal of Communication Systems (Wiley), 2014 Introduction to Internet of Things 8 Nanonetworks  Nanodevice has components of sizes in the order nano‐meters. EL  Communication options among nanodevices  Electromagnetic  Molecular PT N Introduction to Internet of Things 9  EL PT N Source: Akyildiz and Jornet, “Electromagnetic Wireless Nanosensor Networks”, Nano Communication Networks, 2010 Introduction to Internet of Things 10  Molecular Communication  Molecule used as information EL  Information packed into vesicles  Gap junction works as mediator between cells and vesicles  PT Information exchange between communication entities using molecules  Performed at NTT, Japan lab N Sources: Jornet and Akyildiz, “Graphene‐based plasmonic nano‐antenna for terahertz band communication in nanonetworks”, IEEE JSAC, 2013 S. Hiyama, Y. Masitani, T. Suda, “Molecular transport system in molecular communication”, NTT Documo Technical Journal, Vol. 10, No. 3 Introduction to Internet of Things 11  Electromagnetic-based Communication  Surface Plasmonic Polarition EL (SPP) generated upon electromagnetic beam  EM communication for Nanonetworks centers around 0.1‐10 Terahertz channel PT N Sources: Jornet and Akyildiz, “Graphene‐based plasmonic nano‐antenna for terahertz band communication in nanonetworks”, IEEE JSAC, 2013 S. Hiyama, Y. Masitani, T. Suda, “Molecular transport system in molecular communication”, NTT Documo Technical Journal, Vol. 10, No. 3 Introduction to Internet of Things 12  Underwater Acoustic Sensor Networks In a layered shallow oceanic region, the inclusion of the effect of internal solitons on the performance of the network is important. EL Based on various observations, it is proved that non-linear internal waves, i.e., Solitons are one of the major scatters of underwater sound. PT If sensor nodes are deployed in such type of environment, inter-node communication is affected due to the interaction of wireless acoustic signal with these solitons, as a result of which network performance N is greatly affected. Source: A. Mandal, S. Misra, M. K. Dash, T. Ojha, "Performance Analysis of Distributed Underwater Wireless Acoustic Sensor Networks in the Presence of Internal Solitons", International Journal of Communication Systems (Wiley) Introduction to Internet of Things 13  Oceanic forces and their impact The performance analysis of UWASNs renders meaningful insights with the inclusion of a mobility model which represents realistic EL oceanic scenarios. The existing works on performance analysis of UWASNs lack the consideration of major dominating forces, which offer impetus for a node’s mobility. PT The existing works are limited to only shallow depths and coastal areas. Therefore, in this paper, Mandal et al. used a physical mobility model, named oceanic forces mobility model (OFMM), by incorporating important realistic oceanic forces imparted on nodes. N In this model, nodes move in 3D ocean column. Source: A. K. Mandal, S. Misra, T. Ojha, M. K. Dash, M. S. Obaidat, "Oceanic Forces and their Impact on the Performance of Mobile Underwater Acoustic Sensor Networks",International Journal of Communication Systems (Wiley) Introduction to Internet of Things 14  3-Dimensional Localization in USNA Silent & energy-efficient scheme for mobile UWSNs EL Iterative approach  Less initiators nodes (anchors) required Mobility prediction  Enhanced accuracy PT Only 3 surface anchor nodes required N Source: T. Ojha and S. Misra, "MobiL: A 3-Dimensional Localization Scheme for Mobile Underwater Sensor Networks", Proceedings of the 19th Annual National Conference on Communications (NCC 2013), IIT Delhi, New Delhi, India, Feb. 15-17, 2013. Introduction to Internet of Things 15  HASL: High-Speed AUV-Based Silent Localization for Underwater Sensor Networks Get GPS Beacon EL Start Dead- Reception reckoning Silent Trilateration Broadcast beacon listening Get z from at const. interval Receive pressure Beacon Sending PT ‘Effective’ set of beacon message sensor Location Estimation N Source: T. Ojha and S. Misra, “HASL: High-Speed AUV-Based Silent Localization for Underwater Sensor Networks”, Proceedings of the 9th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness(Qshine 2013), Springer, Greater Noida, India, January 2013. Introduction to Internet of Things 16  Opportunistic localization Objective EL Unlocalized nodes: to localize with minimum localization delay. Localized nodes: select a localized with min. energy consumption. PT transmission power level such that max. no. of nodes can be Perspective of unlocalized node Perspective of localized node N Source: S. Misra, T. Ojha, A. Mondal, “Game-theoretic Topology Control for Opportunistic Localization in Sparse Underwater Sensor Networks”, IEEE Transactions on Mobile Computing, vol. 14, no. 5, pp. 990-1003, 2014. Introduction to Internet of Things 17  A Self-Organizing Virtual Architecture  Tic-tac-toe-arch: A self-organizing EL virtual architecture for underwater sensor networks. Calculating the duration of connectivity PT between the underwater nodes A self-organizing network architecture by utilizing the dynamic formation of virtual N topology Source: T. Ojha, M. Khatua and S. Misra, "Tic-Tac-Toe-Arch: A Self-organizing Virtual Architecture for Underwater Sensor Networks", IET Wireless Sensor Systems, Vol. 3, No. 4, December 2013, pp. 307-316. Introduction to Internet of Things 18  Virtual Topology Formation EL Broadcast “REQ” Receive “RPLY” Best Neighbour Neighbour Finding PT Selection Calculate td Select Neighbour Set the selected node in ‘Active’ mode for td time with Max. td Set Duty N Cycle Introduction to Internet of Things 19  EL PT N Introduction to Internet of Things 20  EL Sensor Networks – Part IV PT Dr. Sudip Misra Associate Professor Department of Computer Science and Engineering IIT KHARAGPUR N Email: [email protected] Website: http://cse.iitkgp.ac.in/~smisra/ Introduction to Internet of Things 1 WSN Coverage  Coverage – area‐of‐interest is covered satisfactorily EL  Connectivity – all the nodes are connected in the network, so that sensed data can reach to sink node  Sensor Coverage studies how to deploy or activate sensors to cover the monitoring area  Sensor placement  Density control  Two modes PT  Static sensors N  Mobile sensors Introduction to Internet of Things 2  Definitions:  Sensing range rs  Transmission range rt EL  Relationship between coverage and connectivity rs  If transmission range  2 * sensing range, PT  coverage implies connectivity  Most sensors satisfy the condition!  Coverage is the main issue P D N S rt Introduction to Internet of Things 3 Coverage  Determine how well the sensing field is monitored or tracked by EL sensors  To determine, with respect to application‐specific performance criteria, PT  in case of static sensors, where to deploy and/or activate them  in case of (a subset of) the sensors are mobile, how to plan the trajectory of the mobile sensors.  These two cases are collectively termed as the coverage problem in N wireless sensor networks. Introduction to Internet of Things 4 Coverage (contd.)  The purpose of deploying a WSN is to collect relevant data for EL processing or reporting  Two types of reporting  event driven  on demand PT  e.g. forest fire monitoring  e.g. inventory control system  Objective is to use a minimum number of sensors and N maximize the network lifetime Introduction to Internet of Things 5 Coverage (contd.)  The coverage algorithm proposed are either centralized or distributed and localized EL  Distributed: Nodes compute their position by communicating with their neighbors only.  Centralized: Data collected at central point and global map computed.  Localized: Localized algorithms are a special type of distributed algorithms where only a subset of nodes in the WASN participate in sensing, communication, and computation.  Sensor deployment methods PT  Deterministic versus random  Sensing and communication ranges  Objective of the problem: maximize network lifetime or minimum number of N sensors. Introduction to Internet of Things 6 Coverage Problems in Static WSNs  Most problems can be classified as EL  Area coverage  Point coverage  Barrier coverage PT N Introduction to Internet of Things 7  Area Coverage  Energy‐efficient random coverage EL  Connected random coverage  A network is connected if any active node can communicate with any other active node PT  Zhang and Hou proved that if the communication range Rc is at least twice the sensing range Rs, then coverage implies connectivity N Source: Zhang and Hou, “Maintaining Sensing Coverage and Connectivity in Large Sensor Networks”, Ad Hoc & Sensor Wireless Networks, Vol. 1, pp. 89‐124, 2005. Introduction to Internet of Things 8 Area Coverage (contd.)  An important observation is that an area is completely EL covered if there are at least two disks that intersect and all crossing are covered  Based on these they proposed a distributed, localized PT algorithm called optimal geographical density control N Introduction to Internet of Things 9 Point Covergae  Objective is to cover a set of points EL  Random point coverage – Distribute sensors randomly, so that every point must be covered by at least one sensor at all times  Deterministic point coverage – Do the same in a deterministic manner. PT N Introduction to Internet of Things 10  Barrier Coverage  1‐barrier coverage – covered by at least 1 sensor EL  2‐barrier coverage – covered by at least 2 sensors  K‐barrier coverage – covered by at least k sensors PT N Introduction to Internet of Things 11 Barrier Coverage (contd.) EL Weak Coverage PT Strong Coverage N Introduction to Internet of Things 12  Coverage Maintenance Crossings  A continuous region R is covered if EL  Exist crossings in R  Every crossing in R is covered  Crossings: intersection points between disk boundaries or between monitored space boundary and disk boundaries  A crossing is covered if it is in the interior region of at least one node’s PT N Not covered coverage disk Source: Zhang and Hou, “Maintaining Sensing Coverage and Connectivity in Large Sensor Networks”, Ad Hoc & Sensor Wireless Networks, Vol. 1, pp. 89‐124, 2005. Introduction to Internet of Things 13  Optimality Conditions  Optimality conditions for P EL minimizing overlap while covering crossings B  If nodes A and B are fixed, A node C should be placed such that OR = OQ O Q PT  If nodes A, B, and C all can change their locations, then OP = OR = OQ  If all nodes have the same sensing range, R C N the distance between them is 3  rs Introduction to Internet of Things 14 Optimal Geographical Density Control (OGDC) Algorithm  A node (A) volunteers as a starting node EL  Broadcasts a message containing  Ideal direction ( randomly selected )  Another node (B) closest to the ideal distance and angle becomes active active PT  A node (C) covering P and closest to the optimal location becomes  Repeatedly cover uncovered crossings with nodes that incur minimum overlap. N  A node sleeps if its coverage area is completely covered Introduction to Internet of Things 15  EL PT N Source: https://www.researchgate.net/figure/269392166_fig2_Fig‐2‐Optimal‐Geographical‐Density‐Control‐OGDC‐algorithm Introduction to Internet of Things 16 Optimal Geographical Density Control (OGDC) Algorithm (contd.)  Select a starting node EL  Each node voluntarily participates with probability p  Chooses a back‐off time randomly PT  If it does not hear anything from its neighbors, declares itself as starting node  Declares its position and preferred A N direction Introduction to Internet of Things 17 Optimal Geographical Density Control (OGDC) Algorithm (contd.)  On receiving message from a starting node EL  Each node computes the deviation from desired position (based on distance and angle) message. PT  Chooses a back‐off time randomly  When back‐off expires, it sends power ON  Then, it declares its position and preferred A N direction Introduction to Internet of Things 18 Optimal Geographical Density Control (OGDC) Algorithm (contd.)  The process continues EL until the entire area is Q covered B A   The nodes already PT covered go to sleep mode P O C N Introduction to Internet of Things 19 Optimal Geographical Density Control (OGDC) Algorithm: Highlights  A node initiates the process with desired distance and angle EL  Other nodes calculates the deviation, and the optimal one is chosen  The process continues for all nodes PT  All covered nodes go to sleep mode  This process is continued at each round N Introduction to Internet of Things 20  EL PT N Introduction to Internet of Things 21  EL Sensor Networks– Part V PT Dr. Sudip Misra Associate Professor Department of Computer Science and Engineering IIT KHARAGPUR N Email: [email protected] Website: http://cse.iitkgp.ac.in/~smisra/ Introduction to Internet of Things 1 Stationary Wireless Sensor Networks  Sensor nodes are static EL  Advantages:  Easy deployment  Node can be placed in an optimized distance—Reduce the total number of nodes  Disadvantages: PT  Easy topology maintenance  Node failure may results in partition of networks N  Topology cannot be change automatically Introduction to Internet of Things 2 Stationary Wireless Sensor Networks (Contd.) EL PT N Introduction to Internet of Things 3 Stationary Wireless Sensor Networks (Contd.) EL PT Failure Failure N Introduction to Internet of Things 4 Stationary Wireless Sensor Networks (Contd.) EL Failure Failure PT Split of networks N Introduction to Internet of Things 5  Stationary Wireless Sensor Networks (Contd.) EL Solution? PT To mobilize the sensor nodes N Mobile Wireless Sensor Networks (MWSN) Introduction to Internet of Things 6 Mobile Wireless Sensor Networks  MWSN is Mobile Ad hoc Network (MANET) EL  Let us remember from previous lectures:‐  MANET‐Infrastructure less network of mobile devices connected wirelessly which follow the self‐CHOP properties  Self‐Configure  Self‐Heal    Self‐Optimize Self‐Protect PT Wireless Sensor Networks‐  Consists of a large number of sensor nodes, densely deployed over an area. N  Sensor nodes are capable of collaborating with one another and measuring the condition of their surrounding environments (i.e. Light, temperature, sound, vibration). Introduction to Internet of Things 7 Mobile Wireless Sensor Networks (contd.) EL MANET PT WSN N MWSN Introduction to Internet of Things 8 Components of MWSN  Mobile Sensor Nodes EL Sink PT N Introduction to Internet of Things 9 Components of MWSN  Mobile Sensor EL Nodes  Sense physical  parameters from the environment PT Sink N Introduction to Internet of Things 10 Components of MWSN  Mobile Sensor Nodes EL  Sense physical parameters  from the environment Sink  PT  When these nodes come  in close proximity of sink, deliver data N Introduction to Internet of Things 11 Components of MWSN  Mobile Sensor Nodes EL  Sense physical parameters from the environment  When these nodes come in Sink PT close proximity of sink, deliver data N Introduction to Internet of Things 12 Components of MWSN  Mobile Sink EL  Moves in order to collect data from sensor nodes Sink PT  Based on some algorithm sink moves to different nodes in N the networks Introduction to Internet of Things 13 Components of MWSN  Mobile Sink EL  Moves in order to collect data from sensor nodes PT  Based on some algorithm sink moves to different nodes in Sink N the networks Introduction to Internet of Things 14 Components of MWSN  Data Mules EL  A mobile entity  Collects the data from sensor nodes Sink  PT  Goes to the sink and delivers the collected data from different N  sensor nodes Introduction to Internet of Things 15 Underwater MWSNs  Senses different parameters under the EL sea or water levels  Can be linked with Autonomous PT Underwater Vehicles (AUVs)  Applications: Monitoring‐marine life, water quality etc. N Introduction to Internet of Things 16 Terrestrial MWSNs  Sensor nodes typically deployed over land EL surface  Can be linked with Unmanned Aerial Vehicles (UAVs) PT  Applications: Wildlife monitoring, surveillance, object tracking N Introduction to Internet of Things 17 Aerial MWSNs  Nodes fly on the air and sense data (physical EL phenomena or multimedia data)  Typical example is Unmanned Aerial Vehicles (UAVs) PT  Applications: Surveillance, Multimedia data gathering N Introduction to Internet of Things 18 Possible Entity as Mobile Nodes in Daily-life  Human EL  Mobility can not be predict  Cell phone can gather information and deliver data to an access point  Vehicles  Sensor equipped on it PT  Sense data from different geographical locations and transmit to road side unit (RSU)  Mobile Robot  Controllable sensor node N  Collect data by predefined instructions  Deliver the data to a specific unit Introduction to Internet of Things 19 Human-centric Sensing  Today, smartphones and PDAs are equipped with several sensors, e.g., EL accelerometer and gyroscope  Miniaturization & proliferation of such devices give rise to new sensing paradigms such as,  Participatory sensing PT  People‐centric sensing  Opportunistic sensing  Basic idea:  Humans carry their devices and move around N  Sensors embedded within the devices record readings  Sensory readings are then transmitted Introduction to Internet of Things 20  Human-centric Sensing (contd.)  Three distinct roles (not necessarily mutually exclusive) played by humans EL  Sensing targets: Humans themselves are sensed, e.g., personal health monitoring  Sensor operators: Humans use sensors and applications in smartphones & PDAs to sense surroundings  Data source: Humans disseminate & collect data without actually using any sensor, e.g.,  Challenges in human‐centric sensing:  Energy of devices  Participant selection PT updates posted in social networking sites N  Privacy of users Source: M. Srivastava, T. Abdelzaher, and B. Szymanski, “Human‐centric sensing,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 370, no. 1958, pp. 176–197, Jan. 2012. Introduction to Internet of Things 21  Participatory Sensing  Proposed by Burke et al., 2006 EL  Distributed sensing by devices carried by humans  Goal: Not just collect data, but allow common people to access data and share knowledge  Collected data provides: PT  Quantitative information, e.g., CO2 level  Endorsement of authenticity, e.g., via geo‐tagged location & timestamp N Source: J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, and M. B. Srivastava, “Participatory sensing,” in Workshop on World‐Sensor‐ Web (WSW’06): Mobile Device Centric Sensor Networks and Applications, Boulder, Colorado, USA, 2006, pp. 117–134. Introduction to Internet of Things 22 Delay Tolerant Networks  Lack of end‐to‐end communication paths EL  High latency  Asymmetric data rates; erroneous channels  WSN and MWSN: PT  Typically assume the availability of end‐to‐end path between any sensor node and BS  We saw data MULEs earlier  Such WSNs, in general, belong to the category of delay N tolerant wireless sensor networks (DT‐WSNs) Introduction to Internet of Things 23  EL PT N Introduction to Internet of Things 24  EL UAV Networks PT Dr. Sudip Misra Associate Professor Department of Computer Science and Engineering IIT KHARAGPUR N Email: [email protected] Website: http://cse.iitkgp.ac.in/~smisra/ Introduction to Internet of Things 1 Features of UAV Networks  Mesh or Star networks. EL  Flexible deployment and management of new services using SDN.  Routing protocol should be adaptive in nature.  Contribute towards greening of the network.   Multi‐tasking. PT Large coverage area.  N Easily reconfigurable for varying missions. Introduction to Internet of Things 2 Key Issues  Frequently change in network topology. EL  Relative position of UAV may change. PT  Malfunctioning of UAVs  Intermittent link nature. N  Lack of suitable routing algorithm. Introduction to Internet of Things 3  Considerations in UAV Networks Feature Single UAV System Multi‐UAV System Failures High Low EL Scalability Limited High Survivability Poor High Speed of Mission Slow Fast Cost Bandwidth required Antenna Complexity of Control PT Medium High Omni‐directional Low High Medium Directional High N Failure to coordinate Low Present Source: Lav Gupta, Raj Jain, and Gabor Vaszkun. "Survey of important Issues in UAV communication networks." IEEE Communications Surveys & Tutorials 18.2 (2015): 1123‐ 1152. Introduction to Internet of Things 4 UAV Network Constraints Frequent link breakages EL Prone to malfunction PT Huge power requirements Very complex N Physically prone to environmental effects: winds, rain, etc. Introduction to Internet of Things 5 UAV Network Advantages High Reliability EL High Survivability Single Malfunction Proof PT Cost Effective Efficient N Speeded up missions Introduction to Internet of Things 6 UAV Network Topology: Star  Typically two types – EL  Star Configuration,  Multi‐star Configuration.  In Star Configuration, UAV is directly connected to the ground station.  In Multi‐star Configuration, UAVs   High latency. PT form multiple star topology. One node from each group connects to the ground station. Highly dependent on ground N station. Star Configuration Multi‐star Configuration Introduction to Internet of Things 7 UAV Network Topology: Mesh  Typically two types – EL  Flat Mesh Network,  Hierarchical Mesh Network.  Flexible   Reliable Nodes are PT interconnected N  More secure Flat Mesh Hierarchical Mesh Configuration Configuration Introduction to Internet of Things 8  UAV Topology Comparison Star Network Mesh Network EL Point‐to‐point Multi‐point to multi‐point Central control point present Infrastructure based may have a control center, Ad hoc has no central control center Infrastructure based Infrastructure based or Ad hoc Not self configuring Single hop from node to central point Devices cannot move freely PT Self configuring Multi‐hop communication In ad hoc devices are autonomous and free to move. In infrastructure based movement is restricted around the control center N Links between nodes and central Inter node links are intermittent points are configured Nodes communicated through central controller Nodes relay traffic for other nodes Introduction to Internet of Things 9 FANETs: Flying Ad Hoc Networks  Network formation using UAVs which EL ensures longer range, clearer line of sight propagation and environment‐resilient communication.  UAVs may be in same plane or organized at varying altitudes. PT  Besides self‐control, each UAV must be aware of the other flying nodes of the FANET to avoid collision.  Popular for disaster‐time and post‐disaster N emergency network establishment. Introduction to Internet of Things 10 FANETs: Flying Ad Hoc Networks (contd.)  Features: EL  FANET Inter‐plane communication  FANET Intra‐plane communication  FANET‐ Ground Station communication   PT FANET‐ Ground Sensor communication FANET‐VANET communication N Introduction to Internet of Things 11 Ad-Hoc FANETs  A2A links for data delivery among UAVs. EL  Heterogeneous radio interfaces can be considered in A2A links, such as XBee‐PRO (IEEE 802.15.4) and Wi‐Fi (IEEE 802.11).  Ground networks may be stationary WSNs or PT   VANETS or Control stations. UAV‐WSN link‐up may be used for collaborative sensing as well as data‐muling. UAV‐VANETS link‐up may be used for visual N guidance, data‐muling and coverage enhancement. Introduction to Internet of Things 12  Gateway Selection in FANETs  Main communication requirements of UAV networks are: EL  Sending back the sensor data.  Receiving the control commands.  Cooperative trajectory planning.  Dynamic task assignments. PT  Number of UAV‐ground remote connections should be controlled to avoid interference.  Reduced nodes in the UAV network should act as gateways, to allow communication between all N UAV and the ground Source: F. Luo et al., "A Distributed Gateway Selection Algorithm for UAV Networks," in IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 1, pp. 22‐33, March 2015. Introduction to Internet of Things 13  Gateway Selection in FANETs (contd.)  Entire UAV network coverage area divided into EL sub‐areas.  Sub‐areas collectively cover the entire communication area.  Size of sub‐area to be controlled and adjusted PT   dynamically. Adjustments based on UAV‐interconnections and derived metrics. The derived metrics are optimized for several N iterations till optimum state is achieved. Source: F. Luo et al., "A Distributed Gateway Selection Algorithm for UAV Networks," in IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 1, pp. 22‐33, March 2015. Introduction to Internet of Things 14  Gateway Selection in FANETs (contd.)  Gateway selection initiated by selection of the EL most stable node in the sub‐area.  Consecutively, the partition parameters are optimized according to topology.  Each UAV acquires the information of all UAVs within its 2 hops. PT N Source: F. Luo et al., "A Distributed Gateway Selection Algorithm for UAV Networks," in IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 1, pp. 22‐33, March 2015. Introduction to Internet of Things 15  Layered Gateway in FANETs  Multi‐layered UAV topologies select one EL gateway.  The gateways from each layer communicate to forward information between layers, as well as from ground control. PT  Will increase the delay between ground control and higher layers.  Not suitable for time‐critical relaying tasks. N Introduction to Internet of Things 16  FANETs & VANETs EL PT N Source: Y. Zhou, N. Cheng, N. Lu and X. S. Shen, "Multi‐UAV‐Aided Networks: Aerial‐Ground Cooperative Vehicular Networking Architecture," in IEEE Vehicular Technology Magazine, vol. 10, no. 4, pp. 36‐44, Dec. 2015. Introduction to Internet of Things 17  FANETs & VANETs (contd.) EL PT N Source: Y. Zhou, N. Cheng, N. Lu and X. S. Shen, "Multi‐UAV‐Aided Networks: Aerial‐Ground Cooperative Vehicular Networking Architecture," in IEEE Vehicular Technology Magazine, vol. 10, no. 4, pp. 36‐44, Dec. 2015. Introduction to Internet of Things 18  Trajectory Control for Increasing Throughput  UAVs with queue occupancy above a EL threshold experience congestion resulting in communication delay.  Control station instructs UAVs to change centers of trajectory. PT   Command given based on traffic at “busy” communication link. To provide enhanced coverage, UAVs may be commanded to change radius of their N trajectories. Source: Fadlullah, Zubair Md, et al. "A dynamic trajectory control algorithm for improving the communication throughput and delay in UAV‐aided networks." IEEE Network 30.1 (2016): 100‐105. Introduction to Internet of Things 19  Trajectory Control for Increasing Throughput (contd.) EL PT N Source: Fadlullah, Zubair Md, et al. "A dynamic trajectory control algorithm for improving the communication throughput and delay in UAV‐aided networks." IEEE Network 30.1 (2016): 100‐105. Introduction to Internet of Things 20  EL PT N Introduction to Internet of Things 21  EL Machine to Machine Communication PT Dr. Sudip Misra Associate Professor Department of Computer Science and Engineering IIT KHARAGPUR N Email: [email protected] Website: http://cse.iitkgp.ac.in/~smisra/ Introduction to Internet of Things 1 Introduction  Communication between machines or devices with computing EL and communication facilities.  Free of any human intervention.  Similar to industrial supervisory control and data acquisition PT systems (SCADA).  SCADA is designed for isolated systems using proprietary solutions, whereas M2M is designed for cross‐platform N integration. Introduction to Internet of Things 2  EL PT N Introduction to Internet of Things 3 M2M Overview Sensors EL Network Information Extraction PT Processing Actuation N Introduction to Internet of Things 4 M2M Applications  Environmental monitoring  Civil protection and public safety EL  Supply Chain Management (SCM)  Energy & utility distribution industry (smart grid)  Intelligent Transport Systems (ITSs)     Healthcare PT Automation of building Military applications Agriculture N  Home networks Introduction to Internet of Things 5  M2M Features  Large number of nodes or devices. EL  Low cost.  Energy efficient.  Small traffic per machine/device.    PT Large quantity of collective data. M2M communication free from human intervention. Human intervention required for operational stability and N sustainability. Source: Kim, Jaewoo, et al. "M2M Service Platforms: Survey, Issues, and Enabling Technologies." IEEE Communications Surveys and Tutorials 16.1 (2014): 61‐76. Introduction to Internet of Things 6  M2M Node Types EL M2M M2M M2M Low Mid High PT End End End N Source: Kim, Jaewoo, et al. "M2M Service Platforms: Survey, Issues, and Enabling Technologies." IEEE Communications Surveys and Tutorials 16.1 (2014): 61‐76. Introduction to Internet of Things 7  Low-end Sensor Nodes  Cheap, and have low capabilities.  Static, energy efficient and simple. EL  Deployment has high density in order to increase network lifetime and survivability. PT  Resource constrained, and no IP support.  Basic functionalities such as, data aggregation, auto configuration, and power saving.  Generally used for environment monitoring applications. N Source: Kim, Jaewoo, et al. "M2M Service Platforms: Survey, Issues, and Enabling Technologies." IEEE Communications Surveys and Tutorials 16.1 (2014): 61‐76. Introduction to Internet of Things 8  Mid-end Sensor Nodes  More expensive than low‐end sensor nodes. EL  Nodes may have mobility.  Fewer constraints with respect to complexity and energy efficiency.  Additional functionalities such as localization, Quality of Service intelligence. PT (QoS) support, TCP/IP support, power control or traffic control, and  Typical application includes home networks, SCM, asset N management, and industrial automation. Source: Kim, Jaewoo, et al. "M2M Service Platforms: Survey, Issues, and Enabling Technologies." IEEE Communications Surveys and Tutorials 16.1 (2014): 61‐76. Introduction to Internet of Things 9  High-end Sensor Nodes  Low density deployment. EL  Able to handle multimedia data (video) with QoS requirements.  Mobility is essential.  Example: smartphones. PT  Generally applied to ITS and military or bio/medical applications. N Source: Kim, Jaewoo, et al. "M2M Service Platforms: Survey, Issues, and Enabling Technologies." IEEE Communications Surveys and Tutorials 16.1 (2014): 61‐76. Introduction to Internet of Things 10  M2M Ecosystem Device Providers EL Internet Service Providers (ISPs) Platform Providers PT Service Providers Service Users N Source: Kim, Jaewoo, et al. "M2M Service Platforms: Survey, Issues, and Enabling Technologies." IEEE Communications Surveys and Tutorials 16.1 (2014): 61‐76. Introduction to Internet of Things 11  EL PT N Introduction to Internet of Things 12  M2M Service Platform (M2SP) EL PT N Source: Kim, Jaewoo, et al. "M2M Service Platforms: Survey, Issues, and Enabling Technologies." IEEE Communications Surveys and Tutorials 16.1 (2014): 61‐76. Introduction to Internet of Things 13 M2M Device Platform  Enables access to objects or devices connected to the Internet EL anywhere and at any time.  Registered devices create a database of objects from which managers, users and services can easily access information.  Manages device profiles, such as location, device type, address, and description. PT  Provides authentication and authorization key management functionalities.  Monitors the status of devices and M2M area networks, and N controls them based on their status. Introduction to Internet of Things 14 M2M User Platform  Manages M2M service user profiles and provides functionalities such as, EL  User registration  Modification  Charging  Inquiry. PT  Interoperates with the Device‐platform, and manages user access restrictions to devices, object networks, or services.  Service providers and device managers have administrative privileges on their devices or networks.  Administrators can manage the devices through device monitoring and N control. Introduction to Internet of Things 15 M2M Application Platform  Provides integrated services based on device collected data‐ EL sets.  Heterogeneous data merging from various devices used for creating new services. PT  Collects control processing log data for the management of the devices by working with the Device‐platform.  Connection management with the appropriate network is N provided for seamless services. Introduction to Internet of Things 16 M2M Access Platform  Provides app or web access environment to users. EL  Apps and links redirect to service providers.  Services actually provided through this platform to M2M devices.  Provides App management for smart device apps.  PT App management manages app registration by developers and provides a mapping relationship between apps and devices.  Mapping function provides an app list for appropriate devices. N Introduction to Internet of Things 17 Non-IP based M2M Network EL PT N Introduction to Internet of Things 18 IP-based M2M Network EL PT N Introduction to Internet of Things 19  M2M Area Network Management Features  Fault tolerant  Scalable EL  Low cost, low complexity  Energy efficient  Dynamic configuration capabilities   Minimized management traffic Application dependence:  Data‐centric application,  Emergency application, PT N  Real‐time application Source: Kim, Jaewoo, et al. "M2M Service Platforms: Survey, Issues, and Enabling Technologies." IEEE Communications Surveys and Tutorials 16.1 (2014): 61‐76. Introduction to Internet of Things 20  EL PT N Introduction to Internet of Things 21