Department of Computer Science Runtime Optimisation in WSNs for Load Balancing Using Pheromone Signalling Ipek Caliskanelli James Harbin Leandro Soares Indrusiak Paul Mitchell David Chesmore Fiona Polack
Outline • Motivation and Background why do we work on load balancing on WSNs? applied techniques- task mapping on WSN • Problem Statement • System Model • Load Balancing Based on Pheromone Signalling Linking biological concepts with WSN Algorithm • Verification • Conclusions 2 2
Motivation and Background What is a wireless sensor node? • Small • Autonomous • Self-powered Each node consist of limited resources: embedded processor, memory, battery, radio transceivers and environmental sensors. 3 3
Motivation and Background Why do we work on load balancing? Processing capabilities Energy restrictions Prevents to achieve the high performance efficiency in terms of service availability and Quality of Service (QoS) 4 4
Motivation and Background We propose task mapping optimisation • to solve the energy VS service availability trade-off We apply dynamic task mapping at runtime • to represent the dynamic nature of the WSN • to provide efficient solutions We use Biological knowledge of the bee colonies • To take the advantage of nature • To mimic - highly self-organised systems - adaptability against changes 5 5
Problem Statement The primary design objectives maximisation of service availability minimisation of energy consumption 6 6
System Model Three layer system model consist of Maping Load Balancing Platform Model Application Model process consists of sensor C onsists of tasks nodes Selection of the QN, pheromone propagation and emission 7 7
System Model § Platform Model Consist of set of nodes, N n i ∈ N n = { mc , e , h } • , memory capacity i i i i energy capacity pheromone level Consist of set of links, L l = { n , n } • , sender l L k ∈ i j k receiver 8 8
System Model § Application Model – DAG representations Consist of set of tasks, T t = { mf , e , et } t i ∈ T • , memory footprint i i i i energy consumption execution time Consist of set of communications, C c = { t , t } c C k ∈ • , sender i j k receiver 9 9
System Model • A service is provided by one or more network nodes, and can be requested or triggered by end users, other nodes or even the environment • Each service consist of multiple tasks • A service is available if all of its tasks can successfully be executed by the network nodes 10 10
Outline ü Motivation and Background ü Problem Statement ü System Model • Load Balancing Based on Pheromone Signalling Linking biological concepts with WSN Algorithm • Verification • Conclusions 11 11
Outline ü Motivation and Background ü Problem Statement ü System Model • Load Balancing Based on Pheromone Signalling Linking biological concepts with WSN Algorithm • Verification • Conclusions 12 12
Biological Background In Bee Colonies; • Honey bees need a queen bee to orchestrate the colony and facilitate social interactions For This Purpose; • Bees developed a special pheromonal system in order to maintain the required harmony and orchestration Works as; • Queen bee stimulates a unique pheromone for the worker bees to realize the absence or presence of the queen bee 13 13
Biological Background • This pheromonal mechanism works whereby the worker bees lick the queen bee and pass the queen substance to each others • If there is no queen substance passed through the worker bees; worker bees will then consider the queen as dead so they will grow a larva and will be feed with royalactin protein **Royalactin protein induces the differentiation of honey bee larvae into queens; increases body size, ovary development and shortened developmental time in honey bees • Whereas if worker bees receive the queen substance, they will know that there is a queen bee to orchestrate the colony and take no action towards building a new queen 14 14
Load Balancing Based on Pheromone Signalling Bees Pheromone Stimulation Sensor Network Queen Bee Sensor node responsible for task mapping and execution (QN) Worker Bees Sensor node (WN) Pheromone Level Parameter used for QN selection Lifetime of Bee Operation Lifetime of the Sensor Node TABLE 1: CORRELATION BETWEEN BEE’S PHEROMONE STIMULATION AND SENSOR NETWORKS 15 15
Load Balancing Based on Pheromone Signalling The objective of Pheromone Signalling (PS) Algorithm is to enable node differentiation at a scale that produces sufficient QNs to handle all the required system functionality to avoid unnecessary redundancy 16 16
Load Balancing Based on Pheromone Signalling By applying Load Balancing Algorithm; • QNs stimulate pheromone • Nodes accumulate pheromone • Each node differentiate itself into QN depending on their pheromone level We formalise the PS algorithm by describing its three parts which are executed on every node of the network • Differentiation cycle • Propagation cycle • Decay cycle 17 17
Load Balancing Based on Pheromone Signalling First Part: Differentiation Cycle Occurs on Periodic Basis § executes on nodes every T QN time units. L ISTING 1: PS DIFFERENTIATION CYCLE § a node checks its current pheromone level hi. 1 every T QN do and will differentiate itself into either a QN or h i threshold QN 2 if ( ) < WN. 3 =true QN i § if a node differentiate itself as a QN, it 4 broadcast hd = {0, h QN } propagates pheromone to its network 5 else QN i neighbourhood. 6 =false 18 18
Load Balancing Based on Pheromone Signalling Second Part: Propagation Cycle Occurs on demand LISTING 2: PS PHEROMONE PROPAGATION 1 When hd is received threshold hopcount 2 if ( ) hd [ 1 ] < h h 3 hd [ 2 ] = + i i K HOPDECAY 4 broadcast hd’ = { } hd [ 1 ] 1 , hd [ 2 ]. + § executes every time a node receives a pheromone dose. § a node checks whether the QN that produces it is sufficiently near for the pheromone to affect it. § if the hd has travelled more hops than the threshold, the node simply discards it. If not, it adds the received dosage of the pheromone to its own pheromone level. 19 19
Load Balancing Based on Pheromone Signalling Third Part: Decay Cycle Occurs on Periodic Basis L ISTING 3: PS DECAY CYCLE • decay of the pheromone level of each node, 1 every T DECAY do . = h h which happens every T DECAY time units to K 2 i i TIME DECAY represent the elapsed time. 20 20
Outline ü Motivation and Background ü Problem Statement ü System Model ü Load Balancing Based on Pheromone Signalling • Verification Simulation infrastructure Advantages vs disadvantages of system-level simulation and real node deployment • Conclusions 21 21
Outline ü Motivation and Background ü Problem Statement ü System Model ü Load Balancing Based on Pheromone Signalling • Verification Simulation infrastructure Advantages vs disadvantages of system-level simulation and real node deployment • Conclusions 22 22
Verification Design Goals; • Short implementation time • Minimum financial cost • High performance efficiency System-level simulation model To achieve the most accurate results real sensor datasheets are used 23 23
Verification Analysed; • Two different size platform models in mesh network topology, to capture the effects of the technique on the scalability Platform Model: 4x4 and 7x7 Mesh Topology Application Model: Three different types of DAG which contains 8, 10 and 14 tasks are designed and referred as Service • The key parameters (decay and propagation period) and showed their importance on the performance • Short term effects on real sensor deployments 24 24
Verification - Real Sensor Deployment • the total number of event detections received over time and the number of packets transmitted in the network in total. • the smaller the numbers of event detection is efficient due to the minimal duplication. 25 25
Verification - Real Sensor Deployment impact of queen hormone threshold threshold QN upon the measured event processing and packet transmission load. differentiation algorithm tolerates a stable state with additional queens in the network. This leads to an approximately 10% increase in the total redundant event processing. Total packet transmissions also increased. 26 26
Verification – System Level Simulation 4x4 Mesh Network 4x4 Mesh Network 100 100 100 90 90 80 80 % Events Detected 70 70 % Alive Nodes 60 60 50 50 40 40 30 30 Idle Baseline 20 Baseline 20 BS BS PS 10 10 PS 0 0 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Time (Week) Time (Week) (a) (b) Experimental results: On 4x4 mesh network topology (a) % Events detected, (b) % Alive nodes. 27 27
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