1. Ultra swarm method for resource management in Internet of things deployed mesh networks
Hariharan Ramalingam & Dr. V. Prasanna Venkatesan
Department of Banking Technology,
School of Management,
Pondicherry University,
Puducherry, India.
PAPER ID: ICDIC-170
9th March 2016

2. Contents Introduction Mesh networks with IoT – Benefits & challenges
Ultra swarm method
Proposed model
Implementation
Conclusion
PAPER ID: ICDIC-170
9th March 2016

3. Introduction Internet of things (IoT)
Physical objects linked to sensors, actuators and controllers act as access point and generate data to internet.
Internet of things has ability to sense and enable data from physical objects and its application covering wider domain application areas.
Topology practices for IoT deployment
P2P, Star, Mesh topologies are used for IoT deployment based on applications.
Wireless protocols engaged are
Zigbee, Wifi, MQTT, BLE
PAPER ID: ICDIC-170
9th March 2016

4. Mesh networks with IoT – Benefits & Challenges
Benefits over other protocols
Self healing – ability to re-route packet to destination in case one node in route fails.
End to End reach – extended range compared to other protocols.
Scalable – add new nodes is feasible.
Adaptable – accommodate wide variety of network based on applications.
Challenges
Lack of interoperability – cannot operate with different protocols.
Protocol dependency – router node architecture can work with zigbee protocol only.
Redundancy – high redundancy in data communicated which affects the speed of the network.
PAPER ID: ICDIC-170
9th March 2016

5. Ultra swarm method Swarm intelligence (SI) Wireless cluster computing
Ultra swarm = Swarm intelligence (SI) + Wireless cluster computing
Swarm intelligence (SI)
Nature inspired intelligence
Enables group co-ordination objectives
Wireless cluster computing
Clusters means partitioning of meaningful subgroup of nodes.
Involves cluster generation and migration.
Has ability to improve compute effectiveness.
Wireless cluster computing
PAPER ID: ICDIC-170
9th March 2016

6. Proposed model Enumerating the nodes for
Transmit & Receive functions
Neighbor node sensing functions – inspired by SI
Sub-grouping/re-grouping for cluster formation
Disabling nodes from existing cluster
Best route path based on resources and distance travelled by packets of data. Route table to be used for guidance of best path.
Initiate transmission and observable parameters are
Efficiency in Power consumption
Traffic density
Node availability
Packet delay
PAPER ID: ICDIC-170
9th March 2016

7. Implementation Idle state (State 0) Sense state (State 1)
Very low power consumed, device is idle by default. Change over of this state is triggered by packet arrival.
Sense state (State 1)
Enabled by packet arrival
Neighbor sense is activated based on SI
Distance between nodes, power available info are updated.
Change over of this state is triggered by readiness.
Cluster state (State 2)
Enabled by readiness of node of prev. state.
Connectivity to router coordinator based on resources.
Best route for packet decides the cluster/sub group formation.
Cluster – Max/Min nodes per cluster decides the no. of clusters formed.
Cluster – deformation of clusters is also decides based on above parameters.
Tx/Rx state( State 3)
Packet of data transmit and receive based on best path route on the available cluster.
PAPER ID: ICDIC-170
9th March 2016

8. Conclusion
Ultra swarm method engaged mesh network for IoT has improvement over traditional mesh network for the following parameters
Power
Traffic density
Throughput
QoS
Availability of nodes
The work is qualitative and future scope has opportunity for Quantitative.
Intercept point of Nature inspired algorithm and Internet of things has huge potential for energy saving, green computing applications.
PAPER ID: ICDIC-170
9th March 2016

9. References
Owen Holland, John Woods, Renzo De Nardi and Adrian Clark, “Beyond Swarm Intelligence: The Ultraswarm”, IEEE Swarm intelligence symposium SIS2005, 2005.
Waqas Tariq Dar, “A systematic literature review on Swarm intelligence”, Research gate, July 2015.
Ossama Younis, Sonia Fahmy, “Distributed clustering for scalable, long-lived sensor networks”, MOBICOM, 2003.
Devarat Kulkarni, Dhananjay Kulkarni, “Mesh network topologies for IoT applications”, electronicsofthings.com,
Chris Fraley, Adrian E. Raftery, “How many clusters? Which clustering method? Answers via model cluster analysis”, The computer journal, Vol 41, no.8, 1998.
Lejiang Guo, Weijiang Wang, Jian Cui, Lan Gao, “A Cluster-based algorithm for energy efficient routing in Wireless sensor networks”, IEEE – Computer society, 2010.
Renato C. Juacaba Neto, Rossana M.C. Andrade, Reinaldo B. Braga, Febrice Theoleyre, Carina T. Oliveira, “Performance issues with routing in multi-channel multi-interface s networks”, IFIP Wireless Days (WD), IEEE, 2014.
Giordano, Weir & Fox, “First course in mathematical modelling”, 2nd edition, Brook/Cole Publishing Company, 1997.
Andrew W. Tanenbaum, “Computer Networks”, 4th edition, Prentice Hall, 2003.
PAPER ID: ICDIC-170
9th March 2016

10. Author Profile
PAPER ID: ICDIC-170
9th March 2016