Efficient allocation of resources in multiple heterogeneous Wireless Sensor Networks Wei Li, Flávia C. Delicato Paulo F. Pires, Young Choon Lee Albert Y. Zomaya, Claudio Miceli Luci Pirmez Journal of Parallel Distributed Computing, 2014 Presented by Group 6 Tianyi Pan

Traditional Wireless Sensor Networks (WSNs) Application specific design Single target application Goal of achieving energy efficiency Limited use of versatile sensor nodes WSNs have the capability to allow nodes to perform different tasks System inefficiency – resource wastage

Resource allocation in IoT Middleware WSNs in IoT Targeting diverse applications Collect and process data from different type of sensor nodes Different application QoS requirements WSN system required to integrate heterogeneous WSNs to perform multiple co-existing WSN applications How to fully utilize the resources? How to maintain energy efficiency? Resource allocation in IoT Middleware

How to assign tasks to sensors? System Architecture APP Tasks How to assign tasks to sensors?

Application Model Application: <𝑉,𝐺,Δ𝑡, 𝐶> tasks area duration confidence Task: <service type, data sensing rate, data sending rate, workload> Confidence: Probability of achieving an application goal when a subset of all tasks are executed. Fire detection application: tasks to detect 𝐶 𝑂 2 ,𝐶𝑂, temperature, smoke 100% confidence when all tasks are executed 30% confidence when only temperature is detected User defined

System Model 𝑘 WSNs 𝑊={ 𝑊 1 , 𝑊 2 ,…, 𝑊 𝑘 } in area 𝐺 Each WSN 𝑖 has a single sink 𝑆 𝑁 𝑖 and 𝑃 sensors Each sensor node belongs to exactly one WSN WSNs may overlap with others in sensing range Sensing precision Impacted by environmental noise, proximity to the target area, number of neighboring nodes Precision 𝑀 𝑖 =1− 𝑁 𝑖 100 , where 𝑁 𝑖 is the noise level in [0,100] Assumptions Sensors have the same ratio interface, communication and sensing ranges Interference not considered Keep track of sensor states: location, residual energy, etc.

Energy Model Communication, sensing and processing modules for each sensor Only consider the case when modules are active Omit processing module in this paper 𝐸= 𝐸 𝑐 + 𝐸 𝑠 Communication cost for transmit 𝑙 bit of data from source to destination: 𝐸 𝑐 𝑙,𝑠𝑟𝑐,𝑑𝑒𝑠 = 𝑢,𝑣 ∈ 𝑃 𝑠𝑟𝑐,𝑑𝑒𝑠 𝐸 𝑙, 𝑑 𝑢𝑣 , 𝐸 𝑙, 𝑑 𝑢𝑣 = 𝐸 𝑒𝑙𝑒𝑐 ×𝑙+ 𝜀 𝑎𝑚𝑝 ×𝑙× 𝑑 𝑢𝑣 2 Sensing cost for service 𝑖 for time period 𝑡 𝐸 𝑠 =𝐸 𝑅 𝑠 𝑖 ×𝑡

Resource Allocation Problem System lifetime The time until the first sensor among all WSNs completely depleted its energy Given: 𝑚 applications, each application 𝐴 𝑥 has 𝑉 𝑥 tasks Find: a mapping Π: 𝑉 𝑥 →𝑃 for each application Such that: the application requirements are met and the system lifetime is maximized

Service and Requirement Analysis Three main types of service Sensing: continuous data reading, detect occasional event within an area Computation: fuse incoming data temporarily/spatially, make local decision, eliminate noise/redundant data, store/forward data Communication: claim usage/duration/bandwidth of selected wireless communication channel, determine route to destination Application/task requirements Data accuracy or precision Network lifetime Balance between accuracy/energy consumption

Fulfilling Application Requirements Data accuracy The best candidate is the one with the least noise Energy conservation Reduce sensing energy: use one sensor to concurrently serve multiple tasks Reduce communication energy: choose the sensor that is closest to a sink Combined: choose the sensor node with maximum weight 𝛼 𝐷 𝐷 𝑏𝑒𝑠𝑡 +𝛽 𝐸 𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 −𝐸 𝐸 𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 𝛼+𝛽=1

The Heuristic Allocation Algorithm App Arrival Decompose app to tasks Task Req.? Combined Precision Weight Select sensor with largest weight Select sensor with least noise Select sensor with least energy cost Last Task? Yes End No, check next task

Performance Analysis - Setup Parameter Value Application arrival rate Poisson distribution with mean 10 Multiple app arrival rate Range [0,1], with 1 being most likely, default 0.1 Max number of apps arriving concurrently 3 Tasks in application [2,6], uniform Workload of application 5,15 , uniform Data sensing rate ∞ (continuous sensing) Data sending rate 1,3 , uniform 𝐸 𝑒𝑙𝑒𝑐 50 𝑛𝐽/𝑏 𝜀 𝑎𝑚𝑝 10 𝑝𝐽/𝑏/ 𝑚 2 Initial energy 10 −5 𝐽 𝛼,𝛽 0.5 Not specified: service types for the tasks, geographical requirement of the tasks, capability of the sensors, noise, topology, how WSNs overlap

System Lifetime

Data Accuracy ●: 10% overlapping ■: 50% overlapping ▲: 90% overlapping

Energy Consumption More overlapping: more chance for data sharing

Testbed Experiments: Sun SPOT Platform Use real energy consumption data

Questions?