Mesh network topologies are becoming increasingly popular in battery-powered wireless sensor networks, primarily because of the extension of network range. However, multihop mesh networks suffer from higher energy costs, and the routing strategy employed directly affects the lifetime of nodes with limited energy resources. Hence when planning routes there are trade-offs to be considered between individual and system-wide battery lifetimes. We present a multiobjective routing optimisation approach using hybrid evolutionary algorithms to approximate the optimal trade-off between the minimum lifetime and the average lifetime of nodes in the network. In order to accomplish this combinatorial optimisation rapidly, our approach prunes the search space using k-shortest path pruning and a graph reduction method that finds candidate routes promoting long minimum lifetimes. When arbitrarily many routes from a node to the base station are permitted, optimal routes may be found as the solution to a well-known linear program. We present an evolutionary algorithm that finds good routes when each node is allowed only a small number of paths to the base station. On a real network deployed in the Victoria & Albert Museum, London, these solutions, using only three paths per node, are able to achieve minimum lifetimes of over 99% of the optimum linear program solution’s time to first sensor battery failure.
The link for the paper: http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00151#.Vv6oZmErJhE
More information on our work can be found on: http://emps.exeter.ac.uk/computer-science/wsn/
VLSI Implementation of 32-Bit Unsigned Multiplier Using CSLA & CLAAIJMTST Journal
In this project we are going to compare the performance of different adders implemented to the multipliers based on area and time needed for calculation. The CLAA based multiplier uses the delay time of 99ns for performing multiplication operation where as in CSLA based multiplier also uses nearly the same delay time for multiplication operation. But the area needed for CLAA multiplier is reduced to 31 % by the CSLA based multiplier to complete the multiplication operation.
Analysis of an Energy-Efficient MAC Protocol Based on Polling for IEEE 802.11...Fabrizio Granelli
Presentation of the paper "Analysis of an Energy-Efficient MAC Protocol Based on Polling for IEEE 802.11 WLANs" at the IEEE International Conference on Communications (IEEE ICC 2015)
More details: (blog: http://sandyclassic.wordpress.com ,
linkedin: https://www.linkedin.com/in/sandepsharma )
2 New Routing algorithm for ad-hoc routing wireless sensor network, mathematical modelling for wireless sensor network 4 models for over all system and 2 models for energy measurement of wireless sensor network
VLSI Implementation of 32-Bit Unsigned Multiplier Using CSLA & CLAAIJMTST Journal
In this project we are going to compare the performance of different adders implemented to the multipliers based on area and time needed for calculation. The CLAA based multiplier uses the delay time of 99ns for performing multiplication operation where as in CSLA based multiplier also uses nearly the same delay time for multiplication operation. But the area needed for CLAA multiplier is reduced to 31 % by the CSLA based multiplier to complete the multiplication operation.
Analysis of an Energy-Efficient MAC Protocol Based on Polling for IEEE 802.11...Fabrizio Granelli
Presentation of the paper "Analysis of an Energy-Efficient MAC Protocol Based on Polling for IEEE 802.11 WLANs" at the IEEE International Conference on Communications (IEEE ICC 2015)
More details: (blog: http://sandyclassic.wordpress.com ,
linkedin: https://www.linkedin.com/in/sandepsharma )
2 New Routing algorithm for ad-hoc routing wireless sensor network, mathematical modelling for wireless sensor network 4 models for over all system and 2 models for energy measurement of wireless sensor network
Performance of CBR Traffic on Node Overutilization in MANETsRSIS International
Mobile Ad hoc networks (MANETs) are power constrained since nodes are operated with limited battery supply. The important technical challenge is to avoid the node overutilization and increase the energy efficiency of each node with increasing traffic. If a node runs out of battery, its ability to route the traffic gets affected and hence, the network lifetime. There has been considerable progress in the battery technology, but not in par with the semiconductor technology. There are various techniques adopt the different approaches to achieve energy efficiency. The proposed approach uses a cost metric for path selection, which is a function of residual battery and current traffic load at a node. Further, the simulation and performance is carried through Qualnet network simulator. From the simulation results, it is observed that the proposed scheme has lower node overutilization with the less CBR connections.
Highly -increasing requirement for mobile and several electronic devices want the use of VLSI circuits which are highly power efficient. The most primitive arithmetic operation in processors is addition and the adder is the most highly used arithmetic component of the processor. Carry Select Adder (CSA) is one of the fastest adders and the structure of the CSA shows that there is a possibility for increasing its efficiency by reducing the power dissipation and area in the CSA. This research paper presents power and delay analysis of various adders and proposed a 32-bit CSA that is implemented using variable size of the combination of adders, thus the proposed Carry select Adder (CSA) which has minimum Delay, and less power consumption hence improving the efficiency and speed of the Carry Select Adder.
Design and implementation of Closed Loop Control of Three Phase Interleaved P...IJMTST Journal
A single-phase, three-level, single-stage power-factor corrected AC/DC converter operated under closed
loop manner is presented. That operates with a single controller to regulate the output voltage and the input
inductor act as a boost inductor to have a single stage power factor correction with good output response. The
paper deals with a new single stage three level ac-dc converter which performs both power factor correction
and voltage regulation in a single stage. The proposed converter has two separate controllers, one for power
factor correction and the other for regulating the output voltage. A comprehensive review of the existing single
stage topologies has been carried out. Then the operating principle, control scheme and the design of the new
converter are presented. The proposed converter is having an input power factor close to unity and better
voltage regulation compared to the conventional ac-dc converter topologies. Proposed topology is evaluated
through Matlab/Simulink platform and simulation results are conferred.
Efficient Design of Ripple Carry Adder and Carry Skip Adder with Low Quantum ...IJERA Editor
The addition of two binary numbers is the important and most frequently used arithmetic process on
microprocessors, digital signal processors (DSP), and data-processing application-specific integrated circuits
(ASIC). Therefore, binary adders are critical structure blocks in very large-scale integrated (VLSI) circuits.
Their effective application is not trivial because a costly carry spread operation involving all operand bits has to
be achieved. Many different circuit constructions for binary addition have been planned over the last decades,
covering a wide range of presentation characteristics. In today era, reversibility has become essential part of
digital world to make digital circuits more efficient. In this paper, we have proposed a new method to reduce
quantum cost for ripple carry adder and carry skip adder. The results are simulated in Xilinx by using VHDL
language.
DESIGN AND IMPLEMENTATION OF LOW POWER ALU USING CLOCK GATING AND CARRY SELEC...IAEME Publication
CPUs in general-purpose personal computers, such as desktops and laptops, dissipate significantly more power in the order of few watts because of their higher complexity and speed. ALU is a fundamental building block of CPU. It does all process related to arithmetic and logic operations. As the operations become more complex, the ALU become more complex, more expensive, takes up more space in the CPU and contributes more power dissipation within the CPU. Hence power consumption of ALU is a major issue in the designing of CPU.
In this presentation the authors (Angel A. Juan, Jarrod Goentzel, Tolga Bektas) discuss the vehicle routing problem with multiple driving ranges. The presentation describes an integer programming formulation and a multi-round heuristic algorithm
that iteratively constructs a solution to the problem. Using a set of benchmarks adapted
from the literature, the algorithm is employed to analyse how distance-based costs are increased when considering ‘greener’ fleet configurations – ie, when using electric vehicles
with different degrees of autonomy.
Incorporate ACO routing algorithm and mobile sink in wireless sensor networks IJECEIAES
Today, science and technology is developing, particularly the internet of things (IoT), there is an increasing demand in the sensor field to serve the requirements of individuals within modern life. Wireless sensor networks (WSNs) was created to assist us to modernize our lives, saving labor, avoid dangers, and that bring high efficiency at work. There are many various routing protocols accustomed to increase the ability efficiency and network lifetime. However, network systems with one settled sink frequently endure from a hot spots issue since hubs close sinks take a lot of vitality to forward information amid the transmission method. In this paper, the authors proposed combining the colony optimization algorithm ant colony optimization (ACO) routing algorithm and mobile sink to deal with that drawback and extend the network life. The simulation results on MATLAB show that the proposed protocol has far better performance than studies within the same field.
Evolutionary Multi-Path Routing for Network Lifetime and Robustness in Wirele...Alma Rahat
Wireless sensor networks frequently use multi-path routing schemes between nodes and a base station. Multi-path routing confers additional robustness against link failure, but in battery-powered networks it is desirable to choose paths which maximise the overall network lifetime - the time at which a battery is first exhausted. We introduce multi-objective evolutionary algorithms to find the routings which approximate the optimal trade-off between network lifetime and robustness. A novel measure of network robustness, the fragility, is introduced. We show that the distribution of traffic between paths in a given multi-path scheme that optimises lifetime or fragility may be found by solving the appropriate linear program. A multi-objective evolutionary algorithm is used to solve the combinatorial optimisation problem of choosing routings and traffic distributions that give the optimal trade-off between network lifetime and robustness. Efficiency is achieved by pruning the search space using k-shortest paths, braided and edge disjoint paths. The method is demonstrated on synthetic networks and a real network deployed at the Victoria & Albert Museum, London. For these networks, using only two paths per node, we locate routings with lifetimes within 3% of those obtained with unlimited paths per node. In addition, routings which halve the network fragility are located. We also show that the evolutionary multi-path routing can achieve significant improvement in performance over a braided multi-path scheme.
reporsitory: https://ore.exeter.ac.uk/repository/handle/10871/23185
Performance of CBR Traffic on Node Overutilization in MANETsRSIS International
Mobile Ad hoc networks (MANETs) are power constrained since nodes are operated with limited battery supply. The important technical challenge is to avoid the node overutilization and increase the energy efficiency of each node with increasing traffic. If a node runs out of battery, its ability to route the traffic gets affected and hence, the network lifetime. There has been considerable progress in the battery technology, but not in par with the semiconductor technology. There are various techniques adopt the different approaches to achieve energy efficiency. The proposed approach uses a cost metric for path selection, which is a function of residual battery and current traffic load at a node. Further, the simulation and performance is carried through Qualnet network simulator. From the simulation results, it is observed that the proposed scheme has lower node overutilization with the less CBR connections.
Highly -increasing requirement for mobile and several electronic devices want the use of VLSI circuits which are highly power efficient. The most primitive arithmetic operation in processors is addition and the adder is the most highly used arithmetic component of the processor. Carry Select Adder (CSA) is one of the fastest adders and the structure of the CSA shows that there is a possibility for increasing its efficiency by reducing the power dissipation and area in the CSA. This research paper presents power and delay analysis of various adders and proposed a 32-bit CSA that is implemented using variable size of the combination of adders, thus the proposed Carry select Adder (CSA) which has minimum Delay, and less power consumption hence improving the efficiency and speed of the Carry Select Adder.
Design and implementation of Closed Loop Control of Three Phase Interleaved P...IJMTST Journal
A single-phase, three-level, single-stage power-factor corrected AC/DC converter operated under closed
loop manner is presented. That operates with a single controller to regulate the output voltage and the input
inductor act as a boost inductor to have a single stage power factor correction with good output response. The
paper deals with a new single stage three level ac-dc converter which performs both power factor correction
and voltage regulation in a single stage. The proposed converter has two separate controllers, one for power
factor correction and the other for regulating the output voltage. A comprehensive review of the existing single
stage topologies has been carried out. Then the operating principle, control scheme and the design of the new
converter are presented. The proposed converter is having an input power factor close to unity and better
voltage regulation compared to the conventional ac-dc converter topologies. Proposed topology is evaluated
through Matlab/Simulink platform and simulation results are conferred.
Efficient Design of Ripple Carry Adder and Carry Skip Adder with Low Quantum ...IJERA Editor
The addition of two binary numbers is the important and most frequently used arithmetic process on
microprocessors, digital signal processors (DSP), and data-processing application-specific integrated circuits
(ASIC). Therefore, binary adders are critical structure blocks in very large-scale integrated (VLSI) circuits.
Their effective application is not trivial because a costly carry spread operation involving all operand bits has to
be achieved. Many different circuit constructions for binary addition have been planned over the last decades,
covering a wide range of presentation characteristics. In today era, reversibility has become essential part of
digital world to make digital circuits more efficient. In this paper, we have proposed a new method to reduce
quantum cost for ripple carry adder and carry skip adder. The results are simulated in Xilinx by using VHDL
language.
DESIGN AND IMPLEMENTATION OF LOW POWER ALU USING CLOCK GATING AND CARRY SELEC...IAEME Publication
CPUs in general-purpose personal computers, such as desktops and laptops, dissipate significantly more power in the order of few watts because of their higher complexity and speed. ALU is a fundamental building block of CPU. It does all process related to arithmetic and logic operations. As the operations become more complex, the ALU become more complex, more expensive, takes up more space in the CPU and contributes more power dissipation within the CPU. Hence power consumption of ALU is a major issue in the designing of CPU.
In this presentation the authors (Angel A. Juan, Jarrod Goentzel, Tolga Bektas) discuss the vehicle routing problem with multiple driving ranges. The presentation describes an integer programming formulation and a multi-round heuristic algorithm
that iteratively constructs a solution to the problem. Using a set of benchmarks adapted
from the literature, the algorithm is employed to analyse how distance-based costs are increased when considering ‘greener’ fleet configurations – ie, when using electric vehicles
with different degrees of autonomy.
Incorporate ACO routing algorithm and mobile sink in wireless sensor networks IJECEIAES
Today, science and technology is developing, particularly the internet of things (IoT), there is an increasing demand in the sensor field to serve the requirements of individuals within modern life. Wireless sensor networks (WSNs) was created to assist us to modernize our lives, saving labor, avoid dangers, and that bring high efficiency at work. There are many various routing protocols accustomed to increase the ability efficiency and network lifetime. However, network systems with one settled sink frequently endure from a hot spots issue since hubs close sinks take a lot of vitality to forward information amid the transmission method. In this paper, the authors proposed combining the colony optimization algorithm ant colony optimization (ACO) routing algorithm and mobile sink to deal with that drawback and extend the network life. The simulation results on MATLAB show that the proposed protocol has far better performance than studies within the same field.
Evolutionary Multi-Path Routing for Network Lifetime and Robustness in Wirele...Alma Rahat
Wireless sensor networks frequently use multi-path routing schemes between nodes and a base station. Multi-path routing confers additional robustness against link failure, but in battery-powered networks it is desirable to choose paths which maximise the overall network lifetime - the time at which a battery is first exhausted. We introduce multi-objective evolutionary algorithms to find the routings which approximate the optimal trade-off between network lifetime and robustness. A novel measure of network robustness, the fragility, is introduced. We show that the distribution of traffic between paths in a given multi-path scheme that optimises lifetime or fragility may be found by solving the appropriate linear program. A multi-objective evolutionary algorithm is used to solve the combinatorial optimisation problem of choosing routings and traffic distributions that give the optimal trade-off between network lifetime and robustness. Efficiency is achieved by pruning the search space using k-shortest paths, braided and edge disjoint paths. The method is demonstrated on synthetic networks and a real network deployed at the Victoria & Albert Museum, London. For these networks, using only two paths per node, we locate routings with lifetimes within 3% of those obtained with unlimited paths per node. In addition, routings which halve the network fragility are located. We also show that the evolutionary multi-path routing can achieve significant improvement in performance over a braided multi-path scheme.
reporsitory: https://ore.exeter.ac.uk/repository/handle/10871/23185
Energy Efficient Clustering and Routing in Mobile Wireless Sensor Networkijwmn
A critical need in Mobile Wireless Sensor Network (MWSN) is to achieve energy efficiency during routing
as the sensor nodes have scarce energy resource. The nodes’ mobility in MWSN poses a challenge to
design an energy efficient routing protocol. Clustering helps to achieve energy efficiency by reducing the
organization complexity overhead of the network which is proportional to the number of nodes in the
network. This paper proposes a novel hybrid multipath routing algorithm with an efficient clustering
technique. A node is selected as cluster head if it has high surplus energy, better transmission range and
least mobility. The Energy Aware (EA) selection mechanism and the Maximal Nodal Surplus Energy
estimation technique incorporated in this algorithm improves the energy performance during routing.
Simulation results can show that the proposed clustering and routing algorithm can scale well in dynamic
and energy deficient mobile sensor network.
On the performance of energy harvesting AF partial relay selection with TAS a...IJECEIAES
Energy scarcity has been known to be one of the most noticeable challenges in wireless communication system. In this paper, the performance of an energy harvesting based partial relay selection (PRS) cooperative system with transmit antenna selection (TAS) and outdated channel state information (CSI) is investigated. The system dual-hops links are assumed to follow Rayleigh distribution and the relay selection is based on outdated CSI of the first link. To realize the benefit of multiple antenna, the amplified-andforward (AF) relay nodes then employs the TAS technique for signal transmission and signal reception is achieved at the destination through maximum ratio combining (MRC) scheme. Thus, the closed-form expression for the system equivalent end-to-end cumulative distribution function (CDF) is derived. Based on this, the analytical closed-form expressions for the outage probability, average bit error rate, and throughput for the delaylimited transmission mode are then obtained. The results illustrated that the energy harvesting time, relay distance, channel correlation coefficient, the number of relay transmit antennas and destination received antenna have significant effect on the system performance. Monte-carol simulation is employed to validate the accuracy of the derived expressions.
Vitality productivity Multipath Routing for Wireless Sensor Networks: A Genet...dbpublications
Abstract-The two factors included for deployment of any Wireless Sensor Network, those factors are efficient energy and fault tolerance. An efficient solution for fault tolerance is the Multipath routing in WSNs. Genetic Algorithm is based on the meta-heuristic search technique. Base station (BS) already prepared routing schedule in its routing table, all the nodes share it with the entire network. In proposed algorithm various parameters are used for efficient fitness function such as distance between sender and receiver nodes, distance between BS to hop node and on the number of hop to send data from next hop node to the BS. Simulation and evaluation are tested with various performance metrics in the proposed algorithm.
Experimental Evaluation of Reverse Direction Transmissions in WLAN Using the ...Fabrizio Granelli
Presentation of the paper "Experimental Evaluation of Reverse Direction Transmissions in WLAN Using the WARP Platform" at the IEEE International Conference on Communications (IEEE ICC 2015)
Particle Swarm Optimization for the Path Loss Reduction in Suburban and Rural...IJECEIAES
In the present work, a precise optimization method is proposed for tuning the parameters of the COST231 model to improve its accuracy in the path loss propagation prediction. The Particle Swarm Optimization is used to tune the model parameters. The predictions of the tuned model are compared with the most popular models. The performance criteria selected for the comparison of various empirical path loss models is the Root Mean Square Error (RMSE). The RMSE between the actual and predicted data are calculated for various path loss models. It turned out that the tuned COST 231 model outperforms the other studied models.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
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Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
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Hybrid Evolutionary Approaches to Maximum Lifetime Routing and Energy Efficiency in Sensor Mesh Networks
1. Hybrid Evolutionary Approaches to Maximum Lifetime
Routing and Energy Efficiency in Sensor Mesh Networks
Evolutionary Computation, 2015
DOI: 10.1162/EVCO a 00151
Alma Rahat
Richard Everson
Jonathan Fieldsend
Computer Science
University of Exeter
United Kingdom
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 1 / 12
2. Wireless Sensors
Autonomous devices
Send data to a central base
station
Environmental or process
monitoring
Industrial
Heritage
Pharmaceuticals
Health-care
Battery powered
Monitor locations that are
difficult to access
Typically left unattended for
long periods of time
pictu
Sensor monitoring showcase environment
in Mary Rose Museum, UK
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 2 / 12
3. Mesh Network and Routing Scheme
Sensors and gateway
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 3 / 12
4. Mesh Network and Routing Scheme
Sensors and gateway
Network connectivity map
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 3 / 12
5. Mesh Network and Routing Scheme
Sensors and gateway
Network connectivity map
Mesh Topology: sensors send data
either directly (e.g. S2 = 2, G ) or
indirectly (e.g. S2 = 2, 5, G ) to
the gateway
Alternative routes
Range extension
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 3 / 12
6. Mesh Network and Routing Scheme
Sensors and gateway
Network connectivity map
Mesh Topology: sensors send data
either directly (e.g. S2 = 2, G ) or
indirectly (e.g. S2 = 2, 5, G ) to
the gateway
Alternative routes
Range extension
A routing scheme for the network
R = S1, S2, S3, S4, S5
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 3 / 12
7. Mesh Network and Routing Scheme
Sensors and gateway
Network connectivity map
Mesh Topology: sensors send data
either directly (e.g. S2 = 2, G ) or
indirectly (e.g. S2 = 2, 5, G ) to
the gateway
Alternative routes
Range extension
A routing scheme for the network
R = S1, S2, S3, S4, S5
Maximise
Average lifetime
Time before the first node exhausts its battery (network lifetime)
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 3 / 12
8. Node Costs
Node’s cost due to a routing
scheme R:
C1 =T1,G + (R2,1 + T1,G)
+ (R3,1 + T1,G)
For all transmissions.
Ti,j Transmission cost at node vi
Rj,i Reception cost at node vi
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 4 / 12
9. Node Costs
Node’s cost due to a routing
scheme R:
C1 =T1,G + (R2,1 + T1,G)
+ (R3,1 + T1,G)
For all transmissions.
Ti,j Transmission cost at node vi
Rj,i Reception cost at node vi
T1,G
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 4 / 12
10. Node Costs
Node’s cost due to a routing
scheme R:
C1 =T1,G + (R2,1 + T1,G)
+ (R3,1 + T1,G)
For all transmissions.
Ti,j Transmission cost at node vi
Rj,i Reception cost at node vi
T1,G
R2,1
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 4 / 12
11. Node Costs
Node’s cost due to a routing
scheme R:
C1 =T1,G + (R2,1 + T1,G)
+ (R3,1 + T1,G)
For all transmissions.
Ti,j Transmission cost at node vi
Rj,i Reception cost at node vi
T1,G
R3,1
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 4 / 12
12. Node Costs
Node’s cost due to a routing
scheme R:
C1 =T1,G + (R2,1 + T1,G)
+ (R3,1 + T1,G)
=u1,GT1,G + u1,2R2,1
+u1,3R3,1
For all transmissions.
Ti,j Transmission cost at node vi
Rj,i Reception cost at node vi
ui,j Edge utilisation between vi &
vj for all routes
u1,GT1,G
u1,2R1,2
u1,3R1,3
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 4 / 12
13. Objectives
Lifetime for node vi :
Li (R) =
Qi
Ei + Ci
Radio communication current
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 5 / 12
14. Objectives
Lifetime for node vi :
Li (R) =
Qi
Ei + Ci
Radio communication currentQuiescent current
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 5 / 12
15. Objectives
Lifetime for node vi :
Li (R) =
Qi
Ei + Ci
Radio communication currentQuiescent current
Remaining battery charge
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 5 / 12
16. Objectives
Lifetime for node vi :
Li (R) =
Qi
Ei + Ci
Radio communication currentQuiescent current
Remaining battery charge
Maximise
Average lifetime: f1(R) =
1
n
n
i=1
Li (R)
Network lifetime: f2(R) = min
i∈[1,n]
Li (R)
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 5 / 12
17. Search Space Size
How big is the search space?
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 6 / 12
18. Search Space Size
Number of possible loopless
paths for node v3: 1
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 6 / 12
19. Search Space Size
Number of possible loopless
paths for node v3: 2
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 6 / 12
20. Search Space Size
Number of possible loopless
paths for node v3: 3
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 6 / 12
21. Search Space Size
Number of possible loopless
paths for node v3: 4
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 6 / 12
22. Search Space Size
Number of possible loopless
paths for node v3: 5
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 6 / 12
23. Search Space Size
Number of possible loopless
paths for node v3: 6
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 6 / 12
24. Search Space Size
Number of possible loopless
paths for node v3: 7
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 6 / 12
25. Search Space Size
Number of possible loopless
paths for node v3: 7
Number of possible routing
schemes:
n
i=1
ai
ai : Number of available routes
from vi to vG
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 6 / 12
26. Search Space Size
Number of possible loopless
paths for node v3: 7
Number of possible routing
schemes:
n
i=1
ai
ai : Number of available routes
from vi to vG
4032 solutions
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 6 / 12
27. Search Space Size
Number of possible loopless
paths for node v3: 7
Number of possible routing
schemes:
n
i=1
ai
ai : Number of available routes
from vi to vG
243 solutions
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 6 / 12
28. Search Space Size
Number of possible loopless
paths for node v3: 7
Number of possible routing
schemes:
n
i=1
ai
ai : Number of available routes
from vi to vG
243 solutions
Shorter paths are expected to
be energy efficient
Limit the number of paths
available to each node by using
k-shortest paths algorithm
[Yen, 1972; Eppstein, 1999]
Maximum search space size: kn
Quicker approximation of
Pareto Front
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 6 / 12
29. Max-Min Lifetime Pruning
With no limits on the number of
routes per node, a linear program (LP)
can be derived to maximise network
lifetime [Chang et al., 2004]
max min
vi ∈V
Li
subject to:
Edge utilisation, uij ≥ 0
Energy usage ≤ available charge
Flow conservation
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 7 / 12
30. Max-Min Lifetime Pruning
Solving LP results in best
network lifetime and associated
edge utilisations
Remove unused edges (grey) to
reduce graph
Apply k-SP to extract search
space Ω
With no limits on the number of
routes per node, a linear program (LP)
can be derived to maximise network
lifetime [Chang et al., 2004]
max min
vi ∈V
Li
subject to:
Edge utilisation, uij ≥ 0
Energy usage ≤ available charge
Flow conservation
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 7 / 12
31. Multi-Objective Evolutionary Algorithm
1: A ← InitialiseArchive() Initialise elite archive randomly
2: for i ← 1 : T do
3: R1, R2 ← Select(A) Select two parent solutions
4: R ← CrossOver(R1, R2)
5: R ← Mutate(R )
6: A ← NonDominated(A ∪ R ) Update archive
7: end for
8: return A Approximation of the Pareto set
Crossover Select paths for each node from parents
Mutation Replace paths randomly from k-shortest paths for some
nodes
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 8 / 12
32. Hybrid Evolutionary Approach
1 Gather connectivity map, G
2 Solve LP and erase unused edges to reduce graph, G
3 Search space pruning
Apply k-SP on G to generate search space Ω
Apply k-SP on G to generate search space Ω
Two stages of optimisation
Separate optimisation: apply MOEA on Ω and Ω ; get resulting
estimated Pareto set A and A
Combined optimisation
Use non-dominated solutions in A ∪ A as the initial archive for
combined stage
Apply MOEA in the combined search space Ω ∪ Ω : resulting
estimated Pareto front is A
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 9 / 12
33. Real Network: The Victoria & Albert Museum
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 10 / 12
34. Real Network: The Victoria & Albert Museum
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 10 / 12
35. Real Network: The Victoria & Albert Museum
1st stage: optimising in Ω and Ω separately
1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
Average Lifetime (years)
NetworkLifetime(years)
ΩΩ
30 nodes + gateway
k = 10; Ω and Ω are
limited to 1030
solutions
each.
Initial population size:
100
Mutation and crossover
rate: 0.1
Number of iterations:
150, 000 (1st
stage) and
500, 000 (2nd
stage).
Run time: 2 minutes (1st
stage) and 4 minutes
(2nd
stage).
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 10 / 12
36. Real Network: The Victoria & Albert Museum
1st stage: optimising in Ω and Ω separately
1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
Average Lifetime (years)
NetworkLifetime(years)
ΩΩ
30 nodes + gateway
k = 10; Ω and Ω are
limited to 1030
solutions
each.
Initial population size:
100
Mutation and crossover
rate: 0.1
Number of iterations:
150, 000 (1st
stage) and
500, 000 (2nd
stage).
Run time: 2 minutes (1st
stage) and 4 minutes
(2nd
stage).
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 10 / 12
37. Real Network: The Victoria & Albert Museum
1st stage: optimising in Ω and Ω separately
1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
Average Lifetime (years)
NetworkLifetime(years)
ΩΩ
30 nodes + gateway
k = 10; Ω and Ω are
limited to 1030
solutions
each.
Initial population size:
100
Mutation and crossover
rate: 0.1
Number of iterations:
150, 000 (1st
stage) and
500, 000 (2nd
stage).
Run time: 2 minutes (1st
stage) and 4 minutes
(2nd
stage).
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 10 / 12
38. Real Network: The Victoria & Albert Museum
1st stage: optimising in Ω and Ω separately
2nd stage: optimising in Ω ∪ Ω
1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
Average Lifetime (years)
NetworkLifetime(years)
Ω ∪ Ω
ΩΩ
30 nodes + gateway
k = 10; Ω and Ω are
limited to 1030
solutions
each.
Initial population size:
100
Mutation and crossover
rate: 0.1
Number of iterations:
150, 000 (1st
stage) and
500, 000 (2nd
stage).
Run time: 2 minutes (1st
stage) and 4 minutes
(2nd
stage).
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 10 / 12
39. Real Network: The Victoria & Albert Museum
1st stage: optimising in Ω and Ω separately
2nd stage: optimising in Ω ∪ Ω
1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
Average Lifetime (years)
NetworkLifetime(years)
Ω ∪ Ω
ΩΩ
30 nodes + gateway
k = 10; Ω and Ω are
limited to 1030
solutions
each.
Initial population size:
100
Mutation and crossover
rate: 0.1
Number of iterations:
150, 000 (1st
stage) and
500, 000 (2nd
stage).
Run time: 2 minutes (1st
stage) and 4 minutes
(2nd
stage).
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 10 / 12
40. Real Network: The Victoria & Albert Museum
0 100000 200000 300000 400000 500000 600000 700000 800000
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.5
2.6
Function Evaluations
Hypervolume Single-stage vs.Two-stage
Ω ∪ Ω
Ω ∪ Ω
Ω
Ω
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 10 / 12
41. Real Network: The Victoria & Albert Museum
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
LifetimeRemaining(years)
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
EdgeUtilisation
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16 17
18
19
20
21
22
23
24
25
26
27
28
29
30
1.92 1.93 1.94 1.95 1.96 1.97 1.98 1.99 2.00 2.01
0.7
0.8
0.9
1.0
1.1
Average lifetime: 2 years
Network lifetime: 0.7 years (node v19)
Avg. Lifetime
Net.Lifetime
Gateway
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 10 / 12
42. Real Network: The Victoria & Albert Museum
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
LifetimeRemaining(years)
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
EdgeUtilisation
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16 17
18
19
20
21
22
23
24
25
26
27
28
29
30
1.92 1.93 1.94 1.95 1.96 1.97 1.98 1.99 2.00 2.01
0.7
0.8
0.9
1.0
1.1
Average lifetime: 1.76 years
Network lifetime: 1.29 years (node v13)
Avg. Lifetime
Net.Lifetime
Gateway
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 10 / 12
43. Real Network: The Victoria & Albert Museum
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
LifetimeRemaining(years)
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
EdgeUtilisation
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16 17
18
19
20
21
22
23
24
25
26
27
28
29
30
1.92 1.93 1.94 1.95 1.96 1.97 1.98 1.99 2.00 2.01
0.7
0.8
0.9
1.0
1.1
Average lifetime: 1.94 years
Network lifetime: 1.11 years (node v21)
Avg. Lifetime
Net.Lifetime
Gateway
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 10 / 12
44. Multipath Routing Schemes
Multiple routes available for each
node for sending data to the base
station
D routes per node (D-RS):
R = R1, R2, . . . , RD
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 11 / 12
45. Multipath Routing Schemes
R1 active until node 1 expires
Node 1
Node 5
Charge
Time
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 11 / 12
46. Multipath Routing Schemes
R1 active until node 1 expires
R2 active until node 5 expires
Node 1
Node 5
Charge
Time
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 11 / 12
47. Multipath Routing Schemes
R1 active for time τ1
2-RS
R1 active until node 1 expires
R2 active until node 5 expires
Node 1
Node 5
Charge
Time
τ1
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 11 / 12
48. Multipath Routing Schemes
R1 active for time τ1
2-RS
R2 active for time τ2
R1 active until node 1 expires
R2 active until node 5 expires
Node 1
Node 5
Charge
Time
τ1 τ2
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 11 / 12
49. Multipath Routing Schemes
R1 active for time τ1
2-RS
R2 active for time τ2
R1 active until node 1 expires
R2 active until node 5 expires
Node 1
Node 5
Charge
Time
τ1 τ2
Optimal time share linear
program
max(τ1 + τ2)
subject to:
Time share, τi ≥ 0
Remaining charge ≥ 0
Linear program solved computa-
tionally for each proposed routing
scheme
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 11 / 12
50. Multipath Routing Schemes
Optimising in Ω and Ω separately
1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
Average Lifetime (years)
NetworkLifetime(years)
ΩΩ
Hybrid evolutionary approach
Evolve 1-RS solutions in Ω
and Ω separately
Evolve D-RS solutions in Ω
and Ω separately
Evolve D-RS solutions in
combined search space
Ω ∪ Ω
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 11 / 12
51. Multipath Routing Schemes
Optimising in Ω and Ω separately
1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
Average Lifetime (years)
NetworkLifetime(years)
ΩΩ
R1
R1, R2, R3
Hybrid evolutionary approach
Evolve 1-RS solutions in Ω
and Ω separately
Evolve D-RS solutions in Ω
and Ω separately
Evolve D-RS solutions in
combined search space
Ω ∪ Ω
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 11 / 12
52. Multipath Routing Schemes
Optimising in Ω and Ω separately
Optimising in combined search space Ω ∪ Ω
1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
Average Lifetime (years)
NetworkLifetime(years)
Ω ∪ Ω
ΩΩ
Hybrid evolutionary approach
Evolve 1-RS solutions in Ω
and Ω separately
Evolve D-RS solutions in Ω
and Ω separately
Evolve D-RS solutions in
combined search space
Ω ∪ Ω
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 11 / 12
53. Multipath Routing Schemes
Optimising in Ω and Ω separately
Optimising in combined search space Ω ∪ Ω
1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
Average Lifetime (years)
NetworkLifetime(years)
Ω ∪ Ω
ΩΩ
Hybrid evolutionary approach
Evolve 1-RS solutions in Ω
and Ω separately
Evolve D-RS solutions in Ω
and Ω separately
Evolve D-RS solutions in
combined search space
Ω ∪ Ω
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 11 / 12
54. Multipath Routing Schemes
Optimising in Ω and Ω separately
Optimising in combined search space Ω ∪ Ω
1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
Average Lifetime (years)
NetworkLifetime(years)
Ω ∪ Ω
ΩΩ
98.4% Hybrid evolutionary approach
Evolve 1-RS solutions in Ω
and Ω separately
Evolve D-RS solutions in Ω
and Ω separately
Evolve D-RS solutions in
combined search space
Ω ∪ Ω
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 11 / 12
56. Summary
Multi-objective optimisation of
routing schemes to extend battery
powered mesh network lifetime
Novel search space pruning based
on exact solution from solving a
linear program for network lifetime
Two-stage evolutionary approach to
better approximate the trade-off
between network lifetime and
average lifetime
Optimal time distribution between
multiple routing schemes to achieve
improved network lifetime
About 22% overall performance
gain compared to previous results
510152025
Robustness
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
NetworkLifetime(years)
1-RS
2-RS
Current Work
Estimate the trade-off between
network lifetime and robustness
(tolerance against edge failure)
Rahat, Everson & Fieldsend Max. Lifetime Routing and Energy Efficiency GECCO, July 2015 12 / 12