IN
DEGREE PROJECT MECHANICAL ENGINEERING, SECOND CYCLE, 30 CREDITS
,
STOCKHOLM SWEDEN 2016
Design of a dynamic distributed
CPS application for unmanned
ground vehicles
ERIK BERGDAHL
ANDERS ÅSTRÖM
KTH ROYAL INSTITUTE OF TECHNOLOGY
SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT
MECHATRONICS DEGREE PROJECT IN MECHATRONICS SECOND CYCLE, 30 CREDITS
Utveckling av en dynamiskt
distribuerad CPS applikation f¨
or
obemannade markfordon
Design of a dynamic distributed
CPS application for unmanned
ground vehicles
Erik Bergdahl
Anders ˚
Astr¨
om
Examensarbete inom Mekatronik, Advancerad niv˚a, 30 hpHandledare p˚a KTH: De-Jiu Chen Examinator: Martin T¨orngren TRITA-MMK 2016:170 MDA 570
KTH Skolan f¨or Industriell Teknik och Management
Abstract
New challenges and opportunities arise as a result of an increasing demand of connecting devices in the context of the Internet of Things. Synergy effects can be achieved by con-necting local devices through both wireless local networks and global infrastructures. These thoughts were applied in a military use case where the challenge was identified to reduce the risk of human lives in combat by letting an Unmanned Ground Vehicle (UGV) take over the highest risks during fire control. This thesis covers the design and evaluation of a system that enhances the aiming control performance of a UGV by allocating image processing tasks to an adaptive local wireless distributed computation network.
Background research was conducted through a state of the art survey, literature study and through structured interviews. The system was developed both as a simulation model in TrueTime, and a physical proof of concept demonstrator.
A hypothesis was developed that involves a categorization of two classes of QoS arguments including sample rate and end-to-end delay. These two classes were used as a basis for analyzing the control performance of the UGV in terms of accuracy and precision, and for analyzing the individual impact of the technical parameters of the system. Based on the initial hypothesis, the analysis concludes a potential theoretical speed-up in terms of sample rate of approximately 6 times when implementing wireless distributed computing over Wi-Fi compared, to when the processing tasks are completely managed by the UGV. However, the analysis confirms that the resulting control performance of the UGV is a clear trade-off between the dynamic distribution of computation and communication overheads. Keywords: Distributed computing, wireless distributed computing, computer vision, mobile ad hoc network, parallel processing
Sammanfattning
Nya utmaningar och m¨ojligheter kan identifieras till f¨oljd av en ¨okande efterfr˚agan av att koppla ihop enheter i sammanhanget Internet of Things. Synergieffekter kan uppn˚as genom att koppla ihop lokala enheter b˚ade tr˚adl¨ost och genom global infrastruktur. Dessa tankar applicerades p˚a ett milit¨art scenario d¨ar en utmaning identifierades att re-ducera risken f¨or m¨anskliga liv i strid genom att l˚ata en obemannad markfarkost ¨overta de h¨ogsta riskerna under eldledning. Detta examensarbete har som m˚al att utv¨ardera ett system som f¨orb¨attrar reglerprestandan hos en obemannad markfarkost genom att al-lokera bildbehandlingsuppgifter till ett adaptivt tr˚adl¨ost distribuerat ber¨akningsn¨atverk. Forskningen genomf¨ordes genom en state of the art unders¨okning, litteraturstudie, och strukturerade intervjuer. Systemet utvecklades b˚ade i form av en simuleringsmodell i TrueTime, samt som en fysisk konceptvalidering.
En hypotes togs fram som inneb¨ar en kategorisering av tv˚a klasser f¨or quality of service, samplingshastighet och end-to-end f¨ordr¨ojning. Dessa tv˚a klasser anv¨andes som grund f¨or analysen av reglerprestandan i termer av noggranhet och precision, samt f¨or att analysera de inviduella effekterna av de olika tekniska parametrarna i systemet. Baserat p˚a den initiala hypotesen ¨ar slutsatsen av analysen att en potentiell teoretisk prestanda¨okning i termer av samplingshastighet p˚a 6 g˚anger uppn˚as vid implementering av ett tr˚adl¨ost dis-tribueringsn¨atverk av ber¨akningskraft ¨over Wi-Fi j¨amf¨ort med n¨ar processeringen hanteras enbart av fordonet. Vidare visar ¨aven analysen att den resulterande reglerprestandan ¨ar en tydlig avv¨agning mellan den dynamiska distributionen av ber¨akningsuppgifter och over-head vid kommunikation.
Nyckelord: Distribuerade ber¨akningar, tr˚adl¨ost distribuerade ber¨akningar, datorseende, mobila ad hoc-n¨atverk, parallelprocessering
Acknowledgements
We would initially like to thank Cybercom Group AB for the opportunity to conduct our research under comfortable conditions in their facilities in Kista and our Cybercom supervisors Karl Lund´en and Fredrik Edlund in particular for their advice and support during the thesis project.
Furthermore we would like to thank the individuals that took their time to provide us with valuable information within the military field of fire control.
We would also like to thank our KTH supervisor De-Jiu Chen for his enthusiasm, energy and valuable insights during our meetings. A big thank you goes to Svante Karlsson at Laser Components Nordic AB for providing us with a well functioning small industrial laser pointer for demonstration and evaluation purposes.
We would finally like to thank our friends and our families for their invaluable support during our complete education process and in this project.
Erik Bergdahl & Anders ˚Astr¨om Stockholm June 2016
Table I: Work distribution, see Table of Contents for chapter contents Chapter Main Responsibility Authorship
1 Erik Anders, Erik
2.1 Erik Erik 2.2 Anders Anders 2.3 Anders Anders 3.1 Erik Erik 3.2 Erik Erik 3.3 Anders Anders 3.4 Anders Anders 4 Anders, Erik 5 Anders, Erik 6 Anders Anders 7.1 Anders Anders 7.2 Erik Erik 7.3 Erik Erik 7.4 Anders, Erik 8 Anders, Erik 9 Anders, Erik
Glossary
IoT Internet of Things
RFID Radio-Frequency Identification UGV Unmanned Ground Vehicle UAV Unmanned Aerial Vehicle CPU Central Processing Unit
Scrum Agile project management method OSI Open System Interconnect
PDU Protocol Data Unit QoS Quality of Service
WI-Fi Wireless local area network based on the IEEE 802.11 Protocol MAC Media Access control layer
TCP Transmission Control Protocol IN Interconnection Network
SIMD Single-instruction-stream multiple-data-stream MIMD Multiple-instruction-stream multiple-data-stream) RPi Raspberry Pi
MPI Message Passing Interface SaaS Software as a Service PaaS Platform as Service IaaS Infrastructure as service
WDC Wireless Distributed Computing WSN Wireless Sensor Network
NCS Networked control systems
M2M Machine to Machine communication MANET Mobile ad-hoc Network
ETX Expected transmission count WMN Wireless Mesh Network SRS Swarm Robotic Systems GPS Global Positioning System PSO Particle Swarm Optimization VSN Visual Sensor Networks VR Virtual Reality
DC-Motor Direct Current motor
OpenCV Open Source Computer Vision GPU Graphics Processing Unit
WLAN Wireless Local Area Network E2E End-to-end delay
FPS Frame per second
Iperf Tool for measuring bandwidth IBVS Image Based Visual Servo
Contents
Abstract Sammanfattning Acknowledgement Glossary List of Figures List of Tables 1 Introduction 1 1.1 Background . . . 1 1.2 Purpose . . . 2 1.3 Problem Description . . . 3 1.4 Hypothesis . . . 4 1.5 Delimitations . . . 4 1.6 Method . . . 5 1.6.1 Literature . . . 6 1.6.2 Interviews . . . 6 1.7 Ethics . . . 6 1.8 Report Outline . . . 72 State of the Art 9 2.1 Case-study Anti-Surface Warfare . . . 9
2.1.1 Background . . . 9
2.1.2 Challenges . . . 10
2.2 Mobile M2M Communication . . . 12
2.2.1 Internet of Things . . . 12
2.2.2 Low infrastructure Networks . . . 12
2.2.3 Application Context . . . 14
2.3.1 WDC system model . . . 17
2.3.2 WDC Algorithm . . . 19
3 Theoretical Framework 21 3.1 Communication and Networks . . . 21
3.1.1 Network Topologies . . . 22
3.1.2 Network Protocol Framework . . . 23
3.1.3 Network Performance Metrics . . . 24
3.2 Parallel Computation and Processing . . . 27
3.2.1 Performance theory . . . 27
3.2.2 Distributed Computing . . . 29
3.2.3 Clusters . . . 33
3.2.4 Cloud computing . . . 34
3.3 Object Detection in Image Processing . . . 35
3.3.1 Viola-Jones algorithm . . . 35
3.4 Visual Servo Control . . . 36
4 Design and Implementation 37 4.1 Method . . . 37
4.2 System Specifications . . . 38
4.2.1 Military system . . . 38
4.2.2 System requirements . . . 39
4.2.3 System constraints . . . 40
4.2.4 Unmanned Ground Vehicle . . . 40
4.2.5 Hardware Choices . . . 41
4.2.6 Software Choices . . . 42
4.2.7 Simulation software choice . . . 43
4.3 System Design . . . 44
4.3.1 Overall system design . . . 44
4.3.2 Parallel distribution process . . . 45
4.3.3 Node design . . . 46
4.3.4 Slave node management . . . 48
4.3.5 Motor actuation . . . 51
4.3.6 Simulation model . . . 53
5 Analysis 55 5.1 System parameters . . . 55
5.1.1 Data size and clock rate . . . 56
5.1.2 Bandwidth . . . 57
5.2 Node Utilization . . . 57
5.2.2 Resilience and Redundancy . . . 61 6 Results 65 6.1 Sample rate . . . 65 6.1.1 Simulation . . . 65 6.1.2 Demonstrator . . . 66 6.2 End-to-end delay . . . 66
6.3 Accuracy and precision . . . 68
6.4 Fulfilment of Requirements . . . 68 7 Discussion 71 7.1 Hypothesis revisited . . . 71 7.1.1 Performance evaluation . . . 72 7.2 Context evaluation . . . 72 7.2.1 Ethical evaluation . . . 73 7.2.2 Economical evaluation . . . 73 7.2.3 Sustainability evaluation . . . 74 7.3 Method evaluation . . . 74 8 Conclusion 75 9 Future Work 77 9.1 System protection . . . 77
9.2 Incremental system development and testing . . . 78
9.3 Image partitioning . . . 78
9.4 Node diversity generalization . . . 78
Bibliography 81
List of Figures
1.1 Project Methodology . . . 6
2.1 Mesh network topology where all nodes can connect to another . . . 14
2.2 WDC network system communication graph. Tasks are allocated in a task graph to nodes in a communication graph [17]. . . 18
3.1 The seven layers of the OSI model. . . 23
3.2 Amdahl’s law . . . 27
3.3 Shared memory system where two processors share and access the same memory . . . 30
3.4 Distributed memory architecture in a parallel computer [36] . . . 30
3.5 Different examples of static network topologies in parallel computers . . . . 32
3.6 The crossbars switch is a dynamic network topology consisting of multiple switches in a matrix configuration with multiple inputs and output lines that form a crossed pattern. A connection is established as the switches at each intersection along the line are closed [36]. . . 32
3.7 Beowulf cluster consisting of 32 RPi’s [41] . . . 33
3.8 Typical visual servo control system [49]. . . 36
4.1 Idle nodes in the network analyze sensor data from the vehicle locally before returning the processed data back to the vehicle. All other forms of external communication is avoided in order to prevent the vehicle from giving away its position. . . 39
4.2 Overall system design. . . 44
4.3 Flowchart of the proposed distribution algorithm. . . 45
4.4 The foreground process of the master node. . . 46
4.5 Child handling software design. . . 48
4.6 Motor actuation software design. . . 51
4.7 Coordinate system of a frame. . . 52 4.8 Setup consisting of a TrueTime Wireless Network kernel and a network
with 18 network nodes. The darker block is the master node that distributes processing tasks to the n slaves nodes (a maximum of 17 in this configuration). 54
5.1 The bandwidth along with the data size, affects the transmission time and adds to the total overhead. The frame transmission runs in parallel with the sequential task execution and does not add to the sequential execution time as long as the frame transmission is shorter than the sequential execution. . 58 5.2 Simulation of system network traffic with four connected slave nodes. . . 58 5.3 Simulation of system network traffic with eight connected slave nodes. . . . 59 5.4 Simulation of system network traffic with 23 connected slave nodes. . . 60 5.5 The drop in network bandwidth of the demonstrator system as a function
of distance between two nodes. . . 60 5.6 Simulation of network traffic in surroundings with a high degree of obstacles. 61 5.7 Simulation of system network traffic with losses of nodes. . . 62 5.8 Performance of system versus loss of nodes. . . 62 6.1 Simulated system performance in terms of sample rate in relation to number
of nodes . . . 66 6.2 The resulting achieved frame rate of the demonstrator system (with a
max-imum of four available slave nodes due to the budget constraints presented in section 4.2.2) is compared to the simulated frame rate of the system in relation to number of slave nodes. . . 67
List of Tables
I Work distribution, see Table of Contents for chapter contents . . . . I Raspberry Pi 3 Model B and BeagleBone Black characteristics ([51],[52]) . . 42 II Example of a set of network nodes with different individual parameters. . . 48 III Categorization of a set of nodes depending on their individual parameters. . 49 IV Look-up table . . . 50 I Table depicting the identified system parameters affecting the system
con-trol performance either through the end-to-end delay or sample rate. . . 56 II The approximate increase in time delay resulting from doubling the frame
dimensions (n ∗ m pixels). As illustrated, the sequential execution time and total overhead increase in a linear fashion while the parallel execution time increases exponentially. The time delays were obtained by measuring the execution times of the different segments of the program. . . 57 I The measured frame rate relative to number of slave nodes. . . 66 II The three measured components of the end-to-end delay. . . 67 III Fulfillment of requirements. . . 69
Introduction
This report is the result of a KTH Master Thesis Project in Mechatronics conducted by Erik Bergdahl and Anders ˚Astr¨om during spring 2016. The thesis work was carried out at Cybercom Groups office in Kista outside of Stockholm. This chapter will initially describe the background, purpose and problem description of the project before covering limitations, method and ethics.
1.1
Background
As the number of smart and connected devices increase, new possibilities and challenges arise. The term Internet of Things, or IoT, has in a couple of years gone from a rela-tively unknown phenomenon to a word on everyone’s lips. Many companies now look into the possibilities of designing products that can communicate with other devices and/or be controlled by other computers. This will change the way we live and interact with machines [1]. Most of these connected devices are equipped with small embedded micro-controllers/processors that have strictly limited specifications due to cost constraints. IoT has more to give in terms of possibilities for an architecture of loosely coupled, decentral-ized networks of smart objects that can provide added value to its stakeholders [2].
The authors of [1] define a number of challenges and opportunities within IoT. Among other things they name the challenge of providing analyzed, accurate and on-time infor-mation based on raw sensor data. Important parameters are power and bandwidth for communication with servers etc., but also how to manage computation both horizontally among the devices in the network and vertically with clouds [1]. They also mention the big inertia of development since most projects related to the matter are optimized to a specific application along with slow development of standards and technical solutions [1]. [3] states that IoT represents the future of communication and computation.
Examples of areas of interest for smart systems and networks are applications for military command, control and targeting systems, health care, surveillance, managing inventory, monitoring product quality and monitoring disaster areas [4]. Furthermore, examples of application devices for smart/connected systems range from simple RFID tags mounted
2 | Chapter 1. Introduction
on the surface of a product [5] to Unmanned Ground Vehicles, or UGV’s, and Unmanned Aerial Vehicles, or UAV’s.
Due to the high price of computational power of micro-controllers a variety of cloud ser-vices have been introduced to the market, primarily within storage and computation ap-plications. These cloud services require both large and costly server halls and Internet connection in order to work properly. This can be achieved with wired connections, how-ever, recent research has been advocating a wireless approach to be necessary in future applications [6]. Questions arise how to efficiently distribute and receive information when enormous amounts of devices are connected to the Internet. The prediction of 24 billion connected devices in 2020 [7] imply challenges not only in generating the necessary stor-age and computational resources but also by providing the necessary bandwidth therefore. On the other hand, an opportunity arises to utilize the higher density of products able to communicate with each other in a local context, thus decreasing the demand for cloud computing and bandwidth.
This thesis project aims to investigate the possibilities of distributing heavy computa-tion tasks in dynamic networks consisting of low cost device. Thus, more complex tasks can be solved without Internet connection or high performance CPUs. In the context of military applications within IoT, additional demands apply for the system design. Hence the information security and risk for third party detection is of great importance. Conse-quently, when opposed to an enemy, cloud computing is undesirable in terms of information security. Thus for units dependant on stealthy operations, maintaining a low signal profile as long as possible is of vital importance. Soldiers with the mission to fire-control the impact of heavy weapons such as missiles and mortars are especially vulnerable to enemy countermeasures. Normally these units operate covert and independently which sets high requirements on the soldier and his/her gear. The standard weight that a Swedish fire control soldier has to carry exceeds 50 kg during some missions.
Smart, light devices and unmanned vehicles can contribute in both reducing the risk for casualties but also to enhance endurance and performance of soldiers in battle. They save human lives by reducing human exposure in dangerous situations. Additionally, prop-erly designed machines can provide higher accuracy, i.e. in targeting and following moving threats which are critical during fire-control missions.
1.2
Purpose
A sole non connected device is limited to its own processor, memory and storage. By utilizing the processing capabilities provided by idle nearby units, increasing performance in running computationally complex tasks, reducing battery consumption, weight and cost
3 | Chapter 1. Introduction is a possibility. It is desirable to limit expensive, heavy and energy consuming processors in small mobile military vehicles as well as avoiding external communication and compu-tation to minimize the risk of revealing the vehicle’s position.
The goal of the project is to:
1. Develop and design a proof of concept that involves replacing soldiers performing current fire-control operations with a UGV that can track and aim at targets with computer vision.
2. Research how the control performance of the laser actuation performed by the vehicle is affected by allocating the image processing task locally to nearby devices with processing capabilities using wireless distributed computing, WDC.
3. Find and research which are the set of important technical parameters affecting the control performance when wireless distribution of image processing tasks is per-formed.
The resulting control performance will be analyzed in terms of accuracy and precision when following a moving target with computer vision.
1.3
Problem Description
The thesis will research the following questions:
• RQ1: How is the aiming control performance of an unmanned military vehicle using computer vision affected by wireless distribution of the processing tasks to nearby devices?
Sub research questions:
• SRQ1: Which are the key technical parameters affecting the aiming control perfor-mance of such a system?
• SRQ2: How does the variation of key technical parameters affect the aiming control performance of the system?
The research questions will be studied from a theoretical perspective followed by simula-tions and a physical proof-of-concept implementation. The implementation will be built using existing affordable hardware subsequent to the resulting theoretical study.
4 | Chapter 1. Introduction
1.4
Hypothesis
As the background study was conducted, a hypothesis was developed linking back to the proposed research questions in Section 1.3. The hypothesis involves a categorization of two classes for quality of service metrics during the analysis of the control performance and is presented below.
1. Sample rate: The sample rate is the frame rate achieved by the visual sensor during object detection.
2. End-to-end delay: The end-to-end delay will in this thesis be referred to as the time delay between the sampling of sensor data, i.e. the capturing of an image frame, to the point of actuating the laser.
The impact of each key technical parameter will thus be mapped to the two classes in order to analyze their impact on the control performance.
1.5
Delimitations
This thesis will investigate the possibilities of applying wireless distributed computing in mobile wireless networks in a military context. Since these research fields cover a great span of knowledge, a clear scope and delimitation’s are defined.
The theoretical research will focus on: • Communication and networks
– Communications network characteristics • Parallel computation and processing
– Performance theory – Distributed computing
Moreover, a state of the art research will be conducted which mainly focus on: • Fire-control
• Mobile M2M communication • Internet of Things
5 | Chapter 1. Introduction The control performance will be studied from a computational perspective rather than from a control theoretical approach. Computer vision is the chosen method used to ac-quire and provide sensor data to the system. However, the theoretical research will only briefly touch upon this topic. Already existing solutions and libraries will be implemented rather than developed from scratch.
The scope of this thesis will only briefly cover: • Object detection in image processing • Control theory
The following aspects falls out of the scope of this project: • Security
• Encryption
• Hardware and software robustness corresponding to an actual deployment scenario. Further, the intended network aims to be designed and researched under the limitation that it will be homogeneous and centralized. Hence, all slave nodes will consist of the same hardware, posses equal processing capabilities and function as processing resources rather than sensors.
1.6
Method
The purpose of this study is to explore whether the utilization of nearby idle devices with processing capabilities can enhance the aiming control performance of a military unmanned vehicle using computer vision. The aim is to conduct a pre-study resulting in the design and evaluation of such a system.
The overall objective is to obtain a frame-of-reference via a qualitative literature study on the topics identified together with semi-structured interviews with academic experts and military personnel. The initial phase is supposed to result in a scoped case where the concept identified in the study should be experimentally evaluated with simulations and a physical demonstrator. The implementation method will be experimental and conducted iteratively where quantitative data collected from test-cases will be analyzed and con-cluded for further expansion of the system. The overall project methodology is depicted in Figure 1.1.
6 | Chapter 1. Introduction
Figure 1.1: Project Methodology
1.6.1
Literature
During the literature search the aim is to obtain information that is as contemporary as possible. The main resources used in the literature search are Scopus and IEEE Xplore where Books, articles, conference proceedings etc. are analyzed in order to obtain a diversity of information. Moreover, the aim is to study peer-reviewed literature to the furthest possible extent.
1.6.2
Interviews
Two semi-structured interviews were conducted whose protocols are found in Appendix A. The first interview was conducted with Carlo Fishione, professor at KTH to receive input on the possibilities and limitations regarding WDC. Carlo specializes in network optimization and parallel computation with applications to wireless sensor networks, net-worked control systems, and wireless networks. The second was conducted together with military personnel at the 1st Marine Regiment in Berga south of Stockholm with two soldiers who are experts in fire-control. The second interview was conducted to get a clear picture of the challenges the Swedish military face today in the context of fire-control.
1.7
Ethics
Dealing with resource sharing and wireless communication, there are some ethical as-pects to be considered. It is therefore necessary to investigate certain principles during research and the design of the system. Ethical principles such as privacy, confidentiality and anonymity are probable to be impacted by the proposed system.
Regarding the military application of the research, harm to participants is a major concern both during development and deployment. A thorough ethical analysis is needed in order to evaluate the impact of this technology on a potential battlefield.
7 | Chapter 1. Introduction Potential negative aspects are:
• Enhanced military technology has the potential of being used with the wrong inten-tion thus causing more damage than originally intended
• The risk that military technology ends up in the wrong hands
• Considering that the implementation is a military implementation, security issues and malfunctions have the potential of harming people or causing economical damage Potential positive aspects are:
• The system could have a positive impact from a sustainability perspective, by uti-lizing the computational resources of idle nearby devices thus reducing the need for additional processing hardware
• The concept of a platform overtaking exposure to enemy fire reduces the risk for injuries and casualties among the own soldiers
• Aiming improvements related to machines enhances accuracy leading to a reduced risk for collateral damage
• Overall research on local resource pooling that can aid the enhancement of compu-tational power and smart systems in third world countries or areas lacking Internet connection
There are many interesting and important ethical aspects to be considered. All in all, the technology could assist in minimizing collateral damage to civilians by better precision of aiming, thus constituting a possibility for non-lethal weapon use if used with caution. The potentially improved aiming enables a rethinking in both doctrine and armament due to better precision. For example the precision of aiming might enable a surface to surface missile to disarm a navy vessel instead of destroying it. By the certainty to hit where it is supposed to, the explosive load could be reduced.
Additionally, by allocating the platform away from own troops, enemy counter-fire would affect the platform more and the troops less. Thus it is deemed that the aim of the research is to reduce the collateral damage of existing weapon systems rather than developing new ones, thus maintaining the requirements of ethical researching.
1.8
Report Outline
The outline of the thesis is organized as follows:
1. Introduction: Covering the background, purpose, problem description, hypothesis, delimitation’s, method and ethics of the thesis
8 | Chapter 1. Introduction
2. State of the Art: Covering the application context and use-cases of the technology 3. Theoretical Framework: Covering the theoretical background in communication
and networks, parallel computation and processing, and image processing
4. Design and Implementation: Covering the design and implementation process of the simulation model and demonstrator
5. Analysis: Covering the analysis of the designed demonstrator and simulation model in line with the stated research.
6. Results: Covering the results of the measurements conducted on the simulation model and demonstrator
7. Discussion: Covering the analysis of the results
8. Conclusion: Covering the overall conclusions drawn from the research 9. Future Work Covering the identified subjects for further research
State of the Art
2.1
Case-study Anti-Surface Warfare
Two structured interviews were conducted with military personnel at the 1st Marine Reg-iment in Berga, Sweden on May 16th 2016. The first interview was conducted with a fire-control squadron leader serving in 203 Amfskyttekomp. The second interview was conducted with an indirect fire development officer. The aim of the interviews was to obtain a clear picture of how soldiers operate in the field and to get first hand information on the challenges they encounter performing their duty. The result of the interview is pre-sented in this section. The questions of the structured interviews are found in Appendix A.
2.1.1
Background
A fire-control group is constituted of a number of soldiers with the main task to control the firing from a mortar or missile team. A secondary task is reconnaissance where the group operates more as a ranger squad with the main purpose to collect intelligence. The group is always operating independently from other forces and cannot count on help or logistics from the rest of the battalion during operations. The group therefore needs to bring all equipment needed to fulfill its mission. Depending on mission the weight carried of each individual soldier can range up to 52 kg excluding rifle and uniform. The group uses the Combat Boat 90 for advancing over water, otherwise depending on muscle strength for transportation.
The group primarily uses a Swedish version of AGM-114 Hellfire missiles to defend against incoming enemy vessels. By observing incoming vessels and calculating their speed and direction by hand, the group extracts information that is signaled over radio to the weapon station at another location. When firing is appropriate, an operator from the fire-control group aims a laser on the target as the missile is launched from the weapon station. The missile finds the reflected laser beam guiding it onto the target.
10 | Chapter 2. State of the Art
There are generally three types of communication scenarios in military applications where wired communication is traditionally used. However, as the wireless technology has moved forward an increasing amount of communication have been transitioned to wireless solutions. The advantage of wired communication is that it is hard to jam and eavesdrop from a distant location. The drawback however lies in its lack of mobility. Wireless communication has the inverse problem, it is well suited for mobility but is simultaneously more vulnerable to eavesdropping, triangulation and jamming. These problems can be avoided by using directed communication links. These solutions radiate less signals to their surroundings than normal wireless communication.
2.1.2
Challenges
A group operating in the Swedish archipelago is faced with numerous frictions except for the obvious threat of the enemy. The weather is an important factor due to its infliction on both personnel and gear. Batteries powering radio and laser are sensitive to cold, loosing effect as the temperature drops. The targeting equipment used today is old fashioned, i.e. large and heavy. However it is very robust and handles most situations, i.e. mist, rain etc.
Detection
The highest risk of getting detected occurs during infiltration into the location from where the targeting is supposed to be carried out, and during the actual targeting. The time in between infiltration and fire missions can range from hours up to days, sometimes weeks. During targeting, an operator aims a laser at the enemy vessel. Modern warships however have sophisticated sensors that are able to detect the direction from where the ship is targeted from. It is of uttermost importance for the vessel to protect itself from the missile, but a high priority is also to eliminate the eyes of the laser, e.g. the fire control group. The risk of enemy countermeasures is almost certain within just moments from the time the laser is activated.
High tech vs Old school
An interesting topic that was raised by the second interviewee was the trade-off between the contribution of high-tech solutions versus the risk of their malfunction due to enemy jamming. Consequently, the whole organisation might collapse if it is relying on technology to a high degree. The higher degree of enemy technology is inverse proportional to the technology that can be relied upon in the own organisation. Due to this logic, modern soldiers need to have a knowledge base in doing things old school when faced with an enemy having technology superiority.
11 | Chapter 2. State of the Art Fire vs protection
An optimal situation for the fire-control group would have been to be able to control wa-ter without being detected. The group can be covert for long periods, minimizing their exposure to enemy sensors. However, the moment an enemy vessel has been detected the group will have to increase their exposure in order to enable the chance to stop it. A tactical decision based on the trade-off between damage done and risk for own casualties is taken whether it is profitable for the group to take up the battle or not.
To summarize the conclusions from the interviews conducted at Berga:
1. High risk for personnel: The risks for human lives during contemporary opera-tions are extremely high.
2. Demanding Service: The service conducted by the soldiers is filled with frictions such as heavy gear and hard weather.
3. Complex environments: The Swedish archipelago is constituted by water and islands which induces constraints in mobility.
4. No total control of Unmanned Vehicles: The soldiers cannot totally rely upon technology due to risk of jamming and risk of faults.
12 | Chapter 2. State of the Art
2.2
Mobile M2M Communication
This part will cover the state of the art in Machine to Machine communication, or M2M. It will begin by introducing the background and definition of The Internet of Things as a concept and explore its underlying research trends and future development plan in order to cover a contextual understanding of the theory enabling our thesis project. Subsequently a general overview of networks appropriate for mobile M2M technology and an application context will be presented.
2.2.1
Internet of Things
IoT is a relatively new paradigm which have gained increased attention over the last years. In a historic perspective, the development of communication networks have aimed to connect everyone at any time regardless of location. The aim of IoT however is to connect everything, ranging from simple everyday objects to sophisticated products and systems. The term has been relatively ambiguous and has therefore been both used and misused in a wide variety of applications and concepts[8].
There are no general or absolute definition of the term, semantically IoT means: ”a world-wide network of interconnected objects uniquely addressable, based
on standard communication protocols” [8], p. 2788
Three distinct categorizations have been identified within the field; ”Internet”, ”Things” and ”Semantic” orientation which approaches the paradigm differently. As the Internet approach mainly focuses on connectivity and addressing of objects, Things oriented re-searchers focus on smart items and semantic rere-searchers focus on how to organize, interpret and use the generated data. The IoT paradigm however can be interpreted as the synergy of all three approaches combined [8]. The fundamental idea of the paradigm is to enable these ”things” to sense and interact with the physical world but also being able to share its information to others. A vision is thus to generate a smart environment which enables objects, systems and people to cooperate. The amount of connected devices have already exceeded the amount of people in the world and is estimated to exceed 24 billion in 2020 [9].
2.2.2
Low infrastructure Networks
Due to the operational environment and settings presented in section 2.1 a communication network which is supposed to aid a fire-control group in battle needs to enable mobility and flexibility among its nodes. Moreover this network cannot rely upon a static infrastructure since the battalion cannot guarantee radio coverage in the main combat network. Mobile ad hoc networks and wireless mesh networks are network types that are designed for these kind of requirements and are introduced below.
13 | Chapter 2. State of the Art Mobile Ad Hoc Networks
Ad hoc networks are ”.. a collection of nodes forming a temporary network without the aid of additional infrastructure and no centralized control.” [10] pp 351. The individual nodes of the network can either be static or mobile (the case with mobile nodes is covered thoroughly in the next section). The individual nodes of the network are normally mobile in the sense that they have a battery as power source. However any device that can send and transmit information wirelessly can constitute a node. A difference to other network types is the individual nodes ability to both be able to act as an end system and a router [10].
Mobile Ad Hoc Networks, or MANETs, have inherited the properties of ad hoc net-works. Among the characteristics are their dynamic topology and variable capacity links [11]. [12] describe MANETs distributed properties where individual nodes are permitted to choose the next hop of the route of a packet based on a predefined set of requirements. Moreover, [12] identify MANETS as ideal for Military communication applications due to their self configuring nature and adaptability to create network paths without central management. These abilities enable great mobility within the network. The authors how-ever, also identify drawbacks with MANETS such as variable end-to-end delays and packet losses primarily due to the complex dynamics of the network topology.
There are arguments against MANETs real-time capabilities. [12] believe that there are no guarantees for real-time performance in MANETS due to their network dynam-ics and lack of bandwidth rendering in interference. The unreliability of the real-time functionality is mainly rendered from layer 1 and 2. Instead the authors propose safety critical military systems to operate in inelastic soft real-time requirements which permits a certain amount of E2E delay, intermittent loss and jitter as long as they do not exceed certain boundaries. Moreover, queueing and contention delays are the biggest contributors to the overall E2E delay. Therefore, [12] define various metrics that are commonly used in MANETs, i.e. minimum hop count, routing impact on delay, delay, expected transmission count, medium time metric and worst case execution time.
Wireless Mesh Networks
A Wireless Mesh Network, or WMN, has many similarities with ad hoc networks and MANETs. A WMN consists of mesh routers and clients. All nodes can both host and forward packets. The similarities to ad hoc networks lie within the primary characteristics of WMNs dynamic self- organization, configuring and healing capabilities [13].
Moreover, all nodes can relay to one another as long as they are in range. The topology of node connection is depicted in Figure 2.1 below:
14 | Chapter 2. State of the Art
Figure 2.1: Mesh network topology where all nodes can connect to another In the case where the sender and receiver are out of range from each other, the rest of the network establishes a link between them forwarding the message until it has arrived at the right address. This leads to a mesh routers advantage over a normal Wi-fi router as its capability to achieve the same coverage of the network but with lower power. The disadvantage on the other hand lies within the bandwidth decreasing rapidly through an increased number of nodes in the network [13].
WMN architecture can be categorized in infrastructure or backbone WMNs, client WMNs or as a hybrid between the two. The earlier category consists of one or multiple mesh routers setting up a backbone infrastructure for the mesh clients to operate within. The latter lets the mesh clients arrange their own P2P communication. Critical design factors of WMNs are radio techniques, scalability, mesh connectivity, compatibility and interoperability, security and ease of use among others [13].
2.2.3
Application Context
There is a wide variety of applications connected to the IoT concept. Consequently this part of the thesis is supposed to increase the knowledge on related work and put it in context.
Swarm Robotic Systems
A tendency in recent research has been to introduce swarms of smart devices or robots that cooperate or collaborate in solving various tasks. The main characterization of the objects within the field of Swarm Robotic Systems, or SRS, is that they are of reduced size, have limited access to energy and are highly cost constrained [14].
15 | Chapter 2. State of the Art One of the major challenges within SRS is the localization problem. Due to the robots cost constraints, mounting GPS devices might not be a possible solution. Therefore, a variety of solutions have been researched. A normal approach is to measure the position relative to a reference node with known position, which is called an anchor. The different types of approaches to the localization problem can be divided into two categories, range based and range free. Among the range free approaches are using RFID tags, average position of reference nodes within one hop range and image processing. Range based localization on the other hand uses ad hoc positioning, the Euclidian method and n-hop multiliteration as means of positioning unknown nodes [14].
Sensor Fusion
Due to the lack of high-performance electronics typically used in nodes in the IoT context, sensor fusion technologies are needed. Thus better performance of the entire system can be achieved disregarding the relatively low memory and computational capacity of each node. Already established RFID-tag networks have as an example enhanced the overview of supply chains. Another benefit of the sensor fusion approach is its potential to generate a more comprehensive picture over larger areas than single sensors. Among the sensor data that are of interest in this context are node signal processing, wireless sensor network localization, anti-collision and information aggregation [15]. Following the argumentation of [15], the sensor fusion approach is basically an optimization problem. By implementing an improved Particle Swarm Optimization, or PSO, they believe that they could generate better results than a traditional optimal solution approach. The results also concludes that PSO is a promising technology for implementation in high speed real-time applications in dynamic environments.
Wireless Sensor Networks
Wireless sensor networks are constituted by multiple individual nodes that each have its own power source, sensor, processor and communication module. The characteristics of WSNs are that they are distributed and ad hoc with the purpose to increase the awareness of certain parameters over an area depending on the application [16]. Originally WSNs have been affiliated with systems of sensors that collect one dimensional data such as air pressure or temperature. However, with the improvements of the digital camera over the last decade richer sensor data has been made available. This new richer sensor data implies higher demands and new challenges within transmission and processing. These challenges lie mainly within energy, bandwidth and computational requirements in order to obtain in-time analysis of the system [16].
WSNs based on visual sensor data are called Visual Sensor Networks, or VSNs. Ex-amples of applications within VSNs are remote and distributed video based surveillance, assisted living but also Virtual Reality, or VR. The biggest difference between WSNs and
16 | Chapter 2. State of the Art
VSNs are the amount of information that needs to be transferred and analyzed. Different utilization of individual nodes needs to be addressed depending on the situation, status and the goal of the network. For example if the main goal of the network is to obtain cam-era covcam-erage over an area, decisions regarding which nodes should be active at a given time need to be addressed. Moreover, secondary objectives as network lifetime, connectivity between devices and data gathering need to be optimized[16].
2.3
Wireless Distributed Computing
Multiple collaborative radio nodes operating together form a wireless distributed com-puting (WDC) network. The objective with WDC is to increase performance, operating efficiency and abilities over a single node while reducing per-node and network resource requirements. The goal is also to enable complex applications such as image processing in a network of small form factor radio nodes where such applications are not otherwise possible [17].
The authors in [18] state that the WDC research is driven by limitations in mobile portable computing devices regarding the ability to execute complex mobile applications that are mainly attributed to their resource (energy and computational) constraints. Fur-thermore, workload diversity is achievable by task allocation to various nodes in the net-work where heavily loaded nodes offload their net-workload to nodes with lighter loads based on conditions such as channel conditions and resource availability in the individual nodes. Further, [17] states that driving factors and current trends in WDC are the applications in modern radio systems such as WSNs, tactical radios and commercial smart phones that require networks that are robust and efficient, and can support resource intensive com-putation services. Additionally, future wireless standards are stated to be expected to adopt ad hoc networking approaches for complex computation. Such applications involve complex computation and communication tasks imposing stringent quality of service con-straints such as power consumption, latency and range but also resource (communication, computation and power) requirements.
Since local processing is dependant on a high resource requirement per node and the quality of service guarantee from individual fault prone radio nodes is none, WDC has the potential to enable [17]:
• Decreased power and energy consumption for each node and for the network as a whole
• Efficient allocation of computational resources to meet requirements by sharing the computational workload among collaborating nodes
17 | Chapter 2. State of the Art • Meet computational latency requirements of complex computational tasks be
utiliz-ing and leveragutiliz-ing the network resources
• Fault tolerance in the execution of information services
• Lower resources per node obtained by simplified small form factor node design • Applications employing location based processing
The research related to WDC poses a set of unique challenges over traditional dis-tributed computing. A research question that is fundamental in WDC is the trade-off between distributed and local processing with regard to the specific operational goals such as the minimization of power, energy and time, and the maximization of reliability in processing computational tasks of a specific application. Regarding the practical imple-mentation of collaborative applications in WDC networks, several issues are critical such as robust communication between nodes, synchronization, WDC networks control and management, resource allocation, security and computational accuracy. Communication scheduling and the heterogeneity in the operating environment make resource allocation in WDC a challenge. Though the mobility of a WDC system can implicate several im-provements such as information diversity and geographic flexibility, factors such as channel characteristics and the availability of collaborating nodes can contribute to the negative effects of mobility on WDC performance [17][18].
2.3.1
WDC system model
[17] A WDC environment consists of a number Nnodes wirelessly connected nodes with their individual computational capabilities. Each WDC comprises of computational, munication and power subsystems where the communication subsystem connects the com-putational subsystems on collaborating nodes through wireless links. The communication subsystem serves the distributed computing process in disseminating the computational workload and enables message passing in between computational tasks allocated to the different nodes. [17] presents a WDC network system model depicted in Figure 2.2 con-sisting of graphs where the workload allocation is viewed as a problem of mapping an application’s tasks in the task graph to the network’s communication graph in order to optimize the operational objectives.
18 | Chapter 2. State of the Art
Figure 2.2: WDC network system communication graph. Tasks are allocated in a task graph to nodes in a communication graph [17].
A WDC application is here divided into NF B dependant computational tasks, also called software components. These tasks and the communication between them are rep-resented by a task graph denoted Gcp = (F, E) in Figure 2.2 where the set of tasks are denoted F = (F1, F2, ..., Fm) and the term E represents the set of directed edges. A prece-dence constraint states that a task can only execute when its predecessor task has finished executing. The precedence constraint is represented by an edge (i, j) ∈ E in the depicted task graph and states that Fj can only begin processing once Fi is finished. Thereby, the edges between the vertices represent the dependencies (and precedence constraints) between the respective tasks and communication between the tasks. The edge metric bik represents the interaction between tasks Fi and Fk and indicates the amount of data passed between the tasks in bits.
The communication graph depicted in Figure 2.2 displays a fully connected network of WDC nodes and their respective links. The nodes are denoted N = (N1, N2, ..., Np) and p is the total amount of nodes Nnodes. L is the set of wireless links (graph edges) connecting the nodes. In a network, each node and link is abstracted by its capacity, resource availability and link quality. With message passing, the nodes communicate with each other over one of the available communication channels that each have the bandwidth B. Each edge also has a label indicating parameters such as distance between nodes, channel variance, path loss and transmission rate [17].
19 | Chapter 2. State of the Art
2.3.2
WDC Algorithm
In [17] a task allocation and scheduling algorithm is presented set to optimize energy consumption and makespan. The makespan of a schedule indicates the completion time of the last task in the task graph (i.e. the maximum among the completion times of all tasks in the graph). For the particular algorithm, a resource is considered the best resource for a given task when it can execute that task most efficiently. This requires the usage of priorities to tasks which is based on their estimated level. The estimated level (priority) indicates the order certain tasks have been assigned in the task graph and directly influences the allocation of tasks to nodes and their activity scheduling. The result is that high priority tasks are allocated to the ”best” resources.
Firstly, the estimated level of each task in the task graph is computed. A resource is the considered the ”best” for a given task if it can most efficiently enable the execution of that task. Due to the access constraint, limited availability of resources, and the serial fashion in which tasks are allocated and scheduled to a specific resource, a question will arise concerning the order tasks will be assigned the ”best available” resources. This specific issue is handled by assigning priorities to the tasks based on their individual estimated level. The estimated level works as an indication of the order in which tasks are assigned in the task graph. This way, the estimated level directly affects the allocation of tasks to nodes and the scheduling of the respective activities by allowing high priority tasks to be allocated to the ”best” resources. Consequently, low priority tasks have to wait until their best resources become available again.
Theoretical Framework
This chapter will investigate the theoretical framework in order to enable a critical discus-sion on the results presented in chapter 6. Primarily, this chapter aims to provide to the theoretical background of the core research fields being computer networks, distributed computation and processing, and image processing. The scope is based upon the research questions formulated in section 1.3.
3.1
Communication and Networks
A computer network consists of a number linked computers acting as nodes. Nodes can individually send, receive or route information. The way these nodes are linked together and handle information transmission characterizes different kind of network topologies. There are numerous ways of interconnecting nodes in a computer network.
The transmission medium could either be a physical link such as a wire or wireless where signals travel through the air, water or vacuum via infra-red or radio waves. This thesis is focused on wireless networks in particular due to the requirement of mobility among the nodes. The main differences between wired and wireless network connections do not only lie in the more dynamic nature of the latter but also in the unpredictable environment, medium and limited resources that are connected to wireless technologies. Interferences, obstacles and the mobility of network nodes result in an ever changing and unpredictable variation of signal strength within the network. This naturally inflicts on the quality of the links binding the nodes together in terms of reliability. Data corruption such as packet loss needs to be handled by the network protocol and inflicts both direct delays in terms of resending packets but also indirect delays in terms of built-in over-head of error handling. Moreover, in cases where mobility is critical for the network, new constraints appears. Network nodes then need to be battery powered leading to power consumption constraints. The power consumption and portability constraints are conse-quently often followed by limited memory and processing capacity of individual nodes. Security measures also have to be addressed since unknown devices in range otherwise can retrieve information from the network by eavesdropping[19].
22 | Chapter 3. Theoretical Framework
Since security concerns falls out of the scope of the thesis, this matter will only be briefly mentioned. This chapter aims consequently to obtain the theoretical framework of the factors mentioned above.
3.1.1
Network Topologies
The way nodes are interconnected, either wired or wireless, is called the topology of the network. The aim when designing the network is to generate communication paths for nodes to communicate over. For nodes that do not have a direct link to the node that it needs to communicate with a protocol is needed in order to route the information. In the following sections, a short description of the most common network topologies is given in order to obtain an overview of their differences, advantages and drawbacks. Thereafter a short presentation of routing methods is given.
Star
A network using a star topology contains a central node which operates as a hub. All other nodes are connected to this hub which directs the communication between individual nodes. This topology’s advantage is the few hops the information needs to travel within the network in order to be received at the right address. The disadvantages are the high reliability on the central hub node. If the hub is disconnected, the whole network goes down thus making it impossible for individual nodes to communicate with another [20].
Mesh
A mesh network topology is characterized by the interconnection among nodes. All nodes are connected with all other nodes which enables direct communication between individual nodes without interaction from other nodes. Another advantage with this topology is the network’s overall redundancy to faulty nodes. However, a high cost in wired applications is a disadvantage [20].
The discussion of topologies can be expanded when introducing communication infrastruc-ture as a factor. There are networks that are completely dependant on a communication infrastructure. In this category, nodes can only communicate by using the built-in infras-tructure of the network. As a counterpart, there are networks that do not require any communication infrastructure. These networks are called ad hoc from the Latin expression of something that is created when necessary. There are also combinations of the two, i.e grid networks where the network uses communication infrastructure to connect a wireless network to the Internet [19].
23 | Chapter 3. Theoretical Framework
3.1.2
Network Protocol Framework
Open System Interconnect, or OSI, is a seven layer network model that has become unified reference model of network protocol design [21]. Figure 3.1 depicts the seven layers and their respective placement below:
Figure 3.1: The seven layers of the OSI model.
The OSI model in Figure 3.1 uses a peer-layer strategy where each layer handles data differently from the other layers. The information sent from a layer is meant to be received by the corresponding layer of the receiving device. The other layers are ignoring that information. The data handling of a layer is conducted by a Protocol Data Unit, or PDU. While the host of the network operates within all seven layers of the framework, each node is normally constrained to the bottom three layers [22].
OSI Layers
The Physical Layer is the lowest level of the OSI model and its PDU a single bit. The main objective of the physical layer is to provide the connection and transmission between the data link layers of the sending and receiving devices. Most protocols use voltage, light or electromagnetic signals over varying mediums in order to communicate. Activation and deactivation of a physical connection, PDU transmission, sequencing and physical layer management are the main functions of the physical layer [22].
The second layer of the OSI model is the data link layer which has an increased number of complex functions than the other layers. The PDU of this layer is a frame consisting
24 | Chapter 3. Theoretical Framework
of approximately a hundred to up to a couple of thousand bytes. Control information is added both as header and trailer and this layer supports both connection-oriented and connectionless communication. Among the common functions of the connectionless and connection-oriented data link layer are; error detection, relaying and control of data-circuit interconnection [22].
The third layer of the OSI model is called the Network Layer. Its PDU is a packet consisting of control information and user data. The main objective of the network layer lies within the routing of data and control of the sub-net. Among the functions of the network layer are routing and relaying, network connection, segmentation and blocking, error detection but also network-address-to-data-link-address mapping. The most crucial facilities of the network layer are network addressing, quality of service parameters, expe-dited PDU transfer and error notification. The parameters connected to quality of service are delay, jitter, service availability, reliability and network establishment delay [22].
The fourth layer of the OSI model is called the transport layer. This layer’s respon-sibility is to guarantee the transportation of information from sending to the receiving node when a route has been created by the network layer. In order to achieve this, the transport layer uses error control, flow control and sequence checking in order to obtain a reliable end-to-end transport of the given data [23].
The fifth layer of the OSI-model is called the session layer and is responsible for estab-lishing and terminating connections between nodes of the network.
The sixth layer of the OSI-Model is called the presentation Layer. This layer is re-sponsible for the conversion of data into presentable information. This conversion can take place in the form of decryption, compression, translation or other tasks demanded by the receiving device. [23].
The seventh and highest layer of the OSI-Model is called the application layer. This layer is responsible for guiding applications requesting the different layers of the OSI-Model. It handles file transfers and database access among others [23].
3.1.3
Network Performance Metrics
There are a number of different metrics that can be used depending on the prioritization of qualities in a network. A few of these metrics are presented below.
Quality of Service
Quality of service, or QoS, goes under the definition: ”the group of guarantee parameters which define the behavior of a network under certain conditions” [[24], p. 91]. The term QoS is used in order to measure the performance of the system in terms of predetermined metrics. Normally, a trade-off takes place between QoS and power consumption constraints of the system since they are intimately connected in battery powered devices. The most
25 | Chapter 3. Theoretical Framework common metrics are different measurements of throughput and end-to-end delays.[25] Other metrics especially interesting in ad hoc networks are average end-to-end delays, jitter, packet delivery ratio and average throughput [26].
Package Delay
[27] discusses the need for an understanding of queuing theory in order to optimize overall performance in tactical networks. Moreover, [27] defines four different kinds of delays related to packet transmission namely: Processing, queuing, transmission and propagation delay. Processing delay is defined as the time from arrival of a packet at a layer until it is assigned in an outgoing queue. Queueing delay is defined as the time taken from the point where a packet is placed in the queue until it is transmitted. Transmission delay is defined as the time frame between the transmission of the first and last bit of a packet. Propagation delay is defined as the time between the transmission of the last bit and the reception of the last bit of the packet at the receiver end [27].
Congestion
Network congestion is connected to performance in terms of introducing packet loss and delays due to blockage of network channels. This problem however is intimately connected to the bandwidth of the system but is not the only parameter. The CPU speed of the transmitting router is another parameter along with the size of memory buffer handling queues of packets ready to be transmitted. Problems related to packet loss are correlated with queue drop outs due to both memory shortage and timeout constraints of packets. By aiming to control the flow of the transmissions it is possible to maximize the efficiency of transmissions. The overall aim of these control algorithms are to maximize the utilization of the network, avoid needless congestion and to provide equal service to all connected users [28].
Resilience
Network resilience is the metric related to a systems sensitivity to changes. It is measured as the systems ability to maintain a certain level of service when faced with challenges that intrude on its operational settings. These challenges range from node dropouts to coordinated attacks. The main properties of resilience are reliability, safety, integrity, performance, maintainability, confidentiality and availability [29].
Power consumption
Battery powered devices are common in wireless networks, especially in wireless sensor networks and wireless ad hoc networks. Hence, power consumption is introduced as an important factor both on individual nodes and the overall system.
26 | Chapter 3. Theoretical Framework
Depending on the application, a device drains different amounts of energy. A battery powered device however cannot utilize its full potential at all times. Therefore it conserves energy when possible, iterating over different operating modes which are called power states. When a device is idle it drains the least amount of energy, thus operating in the lowest power state. When the device enters an active mode its power consumption consequently increases [30].
The energy consumed by carrying out a fixed amount of traffic is given by
Etotal = PproductiveTproductive+ PtailTtail (3.1) where E denotes consumed energy, P output power and T the elapsed time [30]. As earlier mentioned, there are challenges introduced when trying to optimize network performance while simultaneously addressing the constraints related to power consumption. From a network perspective, metrics used to measure energy efficiency are network based on the lifetime of alive nodes, coverage, connectivity or until failure to maintain a set of application requirements. The main losses from the network perspective on node level occur during collision of data, node overhearing, control packet overhead, idle listening and interference [31].
Signal strength
The electromagnetic signal strength is important in networks. However the signal strength deteriorates with increased distance from its source and absorption of energy in materials passed. Signal strength can be locally increased or decreased due to superposition of waves. Signal strength within a network is intimately connected to network performance. The probability of packet losses during transmission is increased with a lower signal strength. Depending of the severity, this introduces error handling from the protocol, i.e. Wi-Fi MAC retransmissions and TCP retransmission. Moreover, the relative movement of nodes within a network implies energy consumption when nodes are on the border of connectivity with another. The connection procedure also drains energy in situations when of re-association and hand-offs occur. [30]
27 | Chapter 3. Theoretical Framework
3.2
Parallel Computation and Processing
This section will tackle the dynamics in parallel computing and processing to get a deeper understanding of the possibilities and limitations regarding the subject.
3.2.1
Performance theory
Commonly, software is written and developed in order to be computed serially meaning that the program is written to be executed as a serial stream of instructions. Parallel computing however is simultaneous processing using multiple processing elements. In this case, the problem is divided into several parts where each individual processing element executes its part of the software.
Amdahl’s and Gustafson’s law
However, few parallel computing algorithms are able to achieve a fair fraction of its peak performance [32]. Optimally, the gain in speed resulting from an added number of process-ing units would be linear. Accordprocess-ing to Amdahl’s Law represented graphically in Figure 3.2 below, the speedup is close to linear for a small number of processors and flattens for a higher number. The reason being the sequential component of the computation and the overhead of coordination between the processors such as synchronization.
0 2 4 6 8 10 12 14 16 Number of processors, (2n) 0 2 4 6 8 10 12 14 16 18 20 Speedup Amdahl´s Law 95% Parallel Portion 90% Parallel Portion 75% Parallel Portion 50% Parallel Portion
28 | Chapter 3. Theoretical Framework
The law states that the overall speedup using n number of processors in parallel is given by
SAmdahl(p , n) = 1
1 − p +np (3.2)
where p is the fraction of the whole computation task executed in parallel. Amdahl’s law assumes a fixed sized problem, independent of the number of processors. Therefore the computing requirements remain the same given the increased processing capabilities. [33][34] state that following Amdahl’s law, Gustafson’s law gives a different approach to the theoretical speed in latency but assumes a fixed execution time rather than a fixed workload. For Gustafson’s law the potential overall speedup is given by
SGustaf son(p , n) = 1 − p + pn (3.3) Gustafson states that as computation capabilities grow and computers become more powerful, the problem size grows and subsequently the workload for a potential parallel part of the problem. The reason being that programmers tend to define the size of the problem according to the available amount of processing power. According to Gustafson’s law, the speedup increases in a linear fashion when adding additional processors. Accord-ing to the law, an application will solve bigger problems in the same amount of time rather than solving the same problem in less time. This assumes that the serial portion of the program stays constant which also applies to Amdahl’s law.
[34] states that both laws give accurate representations depending on the situation. However Gustafson’s law tends to follow the historical trend considering the fact that programs solve larger and more complex problems.
Isoefficiency
Isoefficiency is a performance evaluation metric for analyzing parallel algorithm-architecture combinations [35]. The best algorithm for solving a given problem is the fastest algorithm on a sequential computer while the performance of a parallel algorithm for a specific prob-lem provides limited information. Parameters such as probprob-lem size, number of processors, processor speed, speed of communication channels, type of interconnection network and routing techniques affect the overall performance of the parallel algorithm. An algorithm may perform well in one instance for a given problem but may perform poorly if one of these parameters are altered. Scalability is a measure of a parallel systems capability to deliver a linearly increasing speedup with respect to the number of processors used [35]. A parallel systems ability to efficiently utilize an increasing amount of processing nodes is reflected by the the scalability.
