Prof Orhan GemikonakliDr Enver EverDr Leonardo MostardaKrishna Doddapaneni
SENSO LAB is one of the most advanced wireless sensor network labs., hosting hundreds of heterogeneous motes that are deployed around Middlesex University.
More than 20 members
ENERGY AWARE PERFORMANCE EVALUATION OF WSNS
School of Science and TechnologyKrishna Doddapaneni
School of Engineering and Information Sciences
• Wireless sensor networks
• Evaluation methods
• Modeling Framework
• Unequal clustering algorithm – UHEED
• IDS in WSN
• Present work
• Future work
What are they and where are they used
• A large number of small-sensing self-powered nodes gathering information
• Communicating in a wireless fashion with an end-goal to hand their processed data to a base-station
• Key elements: Sensing, Processing & Communication
• Main area of focus - Body Sensors (Hospitals), Bio-Medical, Large-Scale Sport Fields, Industrial Automation
• Gulf oil spill
• No monitoring (use of faulty concrete plugs)
• Mesh-based wireless sensor networks for constant monitoring of the rigs
• Nigerian government funding• WSN for monitoring oil spills• The project went through several stages• The notification is in November• Equipment-provided
• Bridge Monitoring
• In California, 13% of the 23,000 bridges have been deemed structurally deficient, while 12% of the nation's 600,000 bridges share the same rating
• Structural health monitoring (SHM) is a sensor-based pre-emptive approach
• New York may be the first state with a 24/7 wireless bridge monitoring system
• Another application in India: Bri-Mon (Monitoring Railway bridges)
• Life time of sensor node:
2. Sensing module
3. Wireless transmitter/receiver.
Existing studies consider these modules for best deployment, topology, protocol selection, etc.
Energy consumption in a sensor node can be attributed to either “useful” or “wasteful”
Useful energy consumption:
Wasteful energy consumption:
• Transmitting/receiving data.
• Processing query requests.
• Forwarding queries/ data to neighbouring nodes.
• Idle listening.
• Generating/handling control packets.
• Attenuation in power density of an electromagnetic wave as it propagates.
• Path loss is effected by free-space loss, refraction, diffraction, reflection, coupling loss, absorption, propagation medium…..
• Path loss effects should be considered for a more realistic evaluation
Lp is path loss between 2 points
Lo is path loss in Open space
mtype is number of objects of same
wtype is loss in decibels attributed to
d is distance between the points
Path loss calculation
Energy: with and without path loss.
• Achieve high energy efficiency
• Increase the network scalability
• Each cluster has a coordinator(CH) & number of nodes
• Nodes only communicate to their CH.
• Data aggregation, rotation of CH
• Distribution of load across all nodes
Cluster head CH
Thank you !
• Data travels from the source to the destination node via more than two hops.
• Increase the range of the network by a significant margin
It is ineffective to balance loads among cluster heads to avoid hot
spots problem, if the cluster heads are uniformly distributed,
like in HEED.
Unequal clustering algorithm (UHEED)
• Clustering, Multihop Communication,
• Mitigates Hotspots !
• UHEED combine HEED and EEUC • The leader election is performed according to HEED• The radius size is calculated according to EEUC
• Improves network life time.
Equal sized clusters
Unequal sized clusters
• Unequal sized clusters are based on the distance from a cluster head to the base station and energy level.
• The further a cluster head is located from the BS, the larger its competition radius is, and hence the size of the cluster.
• Unequal sized clusters reduce intra-cluster traffic for CH nearer to BS.
is maximum competition radius, predefined.
and are max. and min. distances.
C is constant coefficient between 0 and 1.
The life time of CH closer to BS is more critical, the clusters further away have larger sizes compared to
IDS in WSN
- Promiscuous mode – radio continuously on, to check the correct behaviour of other nodes.
- Hence, lifetime decreases.
- Not really suitable for eventtriggered sensing.
- Agreement Based
- Monitoring based on pre-defined agreement.
- Byzantine oral solution/ signed messages algorithms.
- It is also expensive in terms of no of messages sent and time.
Hence, a need for new approaches, improve Agreement based IDS
The Byzantine Generals Problem
Attack! No, wait! Surrender!
Oral Message Algorithm
Simulation and Results
Case study considered
Radio always On (promiscuous mode)
Byzantine oral message solution
Software Architecture Modelling Language• Set of components that exchange messages• Components have variables manipulated by the behaviour• Behaviour is represented by a list of events, conditions and
Node Modelling Language • Operating system, implemented MAC protocols, routing
protocols• Hardware specification
Environment Modelling Language• The physical environment in which the WSN nodes are
deployed • Obstacles, material….
Weaving models• Mapping Modelling Language• Deployment Modelling Language
This approach provides a clear separation between software components, WSN nodes and the physical environments, thus promoting the reuse of models.
• Expressiveness of Languages : Precision of our abstraction
• Improvise the path loss model
The one we used in our earlier work
Path loss data
• With the formula, we calculate the path loss between two nodes.
• With this data, we explicitly set our path loss map. (its like a matrix, representing the path loss values between the nodes on the network).
• This is done through the SN.wirelessChannel.pathLossMapFile parameter, in Castalia
• Example : 0>1:56,2:40,3:59,4:54,5:58
• This means that when node 0 is transmitting, node 1 is experiencing 56dB path loss, node 2 is experiencing 40dB loss, node 3 a 59dBm loss, etc.
Wireless sensors networks for health care.
Future work: optimal deployment
• Which one is the optimum deployment to improve the network lifetime?
Future work: optimal deployment
• Here we avoid obstacles but nodes must act as routes
A simulator for Wireless Sensor Networks and Body Area Networks, Partly enabled by OMNeT++
For Testing Distributed algorithms, Protocols @ realistic node behaviour, especially relating to access the radio.
Main features include : Advanced channel model Advanced radio model Extended sensing modelling provisions Node clock drift MAC and routing protocols available.
Designed for adaptation and expansion