Green Wireless Video Sensor Networks using a Low-Power Control Channel

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M A P

tele

DOCTORAL PROGRAMME IN TELECOMMUNICATIONS

Green Wireless Video Sensor Networks using a

Low-Power Control Channel

Filipe Miguel Monteiro da Silva e Sousa

A dissertation submitted in partial fulfilment of the requirements for the degree of

Doctor of Philosophy (PhD) in Telecommunications

Supervisor: Manuel Alberto Pereira Ricardo (PhD)

Full Professor at Faculdade de Engenharia da Universidade do Porto Co-Supervisor: Rui Lopes Campos (PhD)

Head of the Wireless Networks Area at INESC TEC

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The Jury

President

Doutor Henrique Manuel de Castro Faria Salgado

Professor Catedrático da Faculdade de Engenharia da Universidade do Porto

Examiners Committee

Doutor Carlos Miguel Tavares de Araújo Cesariny Calafate Professor Catedrático da Universitat Politècnica de València, Espanha

Doutora Marília Pascoal Curado

Professora Associada da Universidade de Coimbra Doutor Adriano Jorge Cardoso Moreira

Professor Associado da Escola de Engenharia da Universidade do Minho Doutor José António Ruela Simões Fernandes

Professor Aposentado da Faculdade de Engenharia da Universidade do Porto Doutor Manuel Alberto Pereira Ricardo

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M A P

tele

DOCTORAL PROGRAMME IN TELECOMMUNICATIONS

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Symbiotic Technology for Societal Efficiency Gains: Deus ex Machina under Grant

NORTE-01-0145-FEDER-000026, both financed by the North Portugal Regional Operational Programme (NORTE 2020), through the PORTUGAL 2020 Partnership Agreement, and the European Regional Development

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Abstract

The availability of low cost networked wireless devices and video cameras is enabling Wireless Video Sensor Networks (WVSNs), which can be used in scenarios such as healthcare, agriculture, smart cities, intelligent transportation systems, and surveillance. These scenarios typically require that each node sends a video stream to a server located in the cloud. IEEE 802.11 is considered a suitable technology for transmitting video wirelessly, as it supports high data rates. However, when using a multi-hop topology to extend IEEE 802.11 coverage, IEEE 802.11-based WVSNs suffer from three problems: low network performance, throughput unfairness, and energy inefficiency. In multi-hop networks, the Carrier Sense Multiple Access – Collision Avoidance (CSMA/CA) leads to low network performance, related to the hidden node problem. Furthermore, the Wireless Video Sensors (WVS) closer to the gateway tend to monopolise the medium making the other WVSs starve, causing throughput unfairness. Energy inefficiency is caused by the low performance of CSMA/CA in multi-hop topologies since frame collisions imply retransmissions. Moreover, a packet is overheard by several nodes in the same broadcast domain even when that node is not the destination, thus wasting energy. Since relay nodes are forced to stay in idle listening most of the time to forward packets from other nodes, energy is also lost.

In this thesis, we propose a holistic solution for WVSNs, named Green wiReless vidEo sENsor NEtworks uSing out-of-band Signalling (GREENNESS), to address these problems. GREENNESS combines a centralised node polling mechanism with out-of-band signalling over a low power radio. The polling mechanism improves the network performance and throughput fairness. The use of a low power radio to convey the signalling, namely the node that shall transmit and the nodes that shall switch OFF their IEEE 802.11 interfaces, saves energy. By using numerical, simulation, and experimental analysis we show that GREENNESS can achieve significant energy savings and improve network capacity, packet loss ratio, and throughput fairness when compared to state-of-the-art CSMA/CA-based WVSN solutions. GREENNESS can save up to 92 % in the energy consumption, guarantee an improvement

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in network capacity above 30 % and perfect throughput fairness.

Keywords: Energy-efficiency, Low Power Radio, Network Performance, Out-of-band Signalling, Wireless Video Sensor Networks.

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Resumo

O surgimento de dispositivos de rede sem fios e de câmaras de vídeo de baixo custo possibilitou o aparecimento das redes de sensores de vídeo sem fios (WVSNs), que podem ser utilizadas para diferentes aplicações como saúde, agricultura, cidades inteligentes, sistemas de transporte inteligentes e videovigilância. Nestes cenários, exige-se normalmente que cada nó envie um fluxo de vídeo para um servidor localizado na nuvem. A tecnologia IEEE 802.11 é considerada adequada para transmissão de vídeo em redes sem fios, já que suporta taxas de transferência de dados elevadas. No entanto, a utilização de uma topologia de rede com vários saltos para aumentar a cobertura de uma WVSN basead em IEEE 802.11 encerra três problemas: baixo desempenho, iniquidade na taxa de transferência de dados e ineficiência energética. Em redes com múltiplos saltos, o Carrier Sense Multiple Access – Collision

Avoidance (CSMA/CA) leva ao baixo desempenho da rede, relacionado com o problema do

nó escondido. Além disso, os sensores de vídeo sem fios (WVS) mais próximos da gateway tendem a monopolizar o meio, fazendo com que os outros WVSs não consigam transmitir os seus dados, causando iniquidade na taxa de transferência de dados. A ineficiência energética é causada pelo baixo desempenho do CSMA/CA em topologias com múltiplos saltos, dado que as colisões das tramas implicam a sua retransmissão. A energia também é desperdiçada quando a transmissão de uma trama é ouvida por vários nós no mesmo domínio de difusão mesmo quando esse nó não é o destino da trama. Como os nós de retransmissão são forçados a permanecer ligados grande parte do tempo para encaminhar pacotes de outros nós, existe um desperdício de energia adicional.

Nesta tese, propomos uma solução holística para WVSNs, denominada Green WiReless vidEo sENsor NEtworks uSing Out-of-band Signaling (GREENNESS) para endereçar os três problemas. O GREENNESS combina um mecanismo de polling centralizado com sinalização fora de banda com recurso a um rádio de baixa potência. O mecanismo de polling melhora o desempenho da rede e a equidade na taxa de transferência de dados. A utilização de um rádio de baixa potência para transmitir a sinalização permite controlar os nós da rede que

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podem transmitir dados e os que devem desligar as suas interfaces IEEE 802.11, poupando desta forma energia. Através de análise numérica, simulação e experimentação demonstramos que o GREENNESS, quando comparado com soluções WVSN do estado da arte baseadas em CSMA/CA, pode alcançar poupanças significativas de energia, melhorar a capacidade da rede e a equidade da taxa de transferência de dados. O GREENNESS pode poupar até 92 % do consumo energético, garantir um aumento da capacidade de rede superior a 30 % e equidade perfeita na taxa de transferência de dados.

Keywords: Eficiência Energética, Radio de Baixo Consumo, Sinalização Fora de Banda, Desempenho da Rede, Redes de Sensores de Vídeo Sem Fios.

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Acknowledgements

First, I would like to thank Prof. Manuel Ricardo for his support and valuable guidance. I also would like to thank Rui Campos for supporting me in this endeavour and useful suggestions that help me to overcome the challenges. I was fortunate for having João Dias and Filipe Ribeiro’s collaboration and support in the development and testing of the prototype. Thanks to Fraunhofer Portugal - AICOS for supporting my involvement in the MAP-Tele doctoral programme and also my close colleagues that encouraged me and motivated to achieve this objective. I would also like to express my thanks to the contributions from many individuals, in numerous public presentations and paper reviews that helped to shape my work. I am grateful to my parents for teaching me to never give up and for their support over the years. I want to extend my thanks to my close friends and family for their understanding and relaxing evenings during this journey. Finally, I would like to thank and dedicate this thesis to my precious treasures and loves of my life Isabel, Mariana, and Inês.

Filipe Sousa

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“Our greatest weakness lies in giving up. The most certain way to succeed is always to try just one more time.”

Edison, Thomas A.

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List of Figures

1.1 Forecast of connected devices with an IP stack [1]. . . 1

1.2 Reference scenario for WVSNs using Wi-Fi cameras in multi-hop network topology and streaming video through a gateway to a cloud server. . . 3

1.3 Hidden node and exposed node problems in IEEE 802.11-based multi-hop networks. . . 4

1.4 The GREENNESS concept, with the node scheduling mechanism running over the LPR control channel illustrated by the arrows in orange. . . 6

2.1 Wireless Video Sensor Architecture with Wake-Up Radio [15]. . . 10

2.2 Wake-Up Radio communication schemes [14]. . . 11

2.3 Tang et al model of a mobile node [17]. . . 12

2.4 SleepyCAM power management solution [26]. . . 14

2.5 Mekonnen et al Multi-Tier Architecture [18]. . . 15

2.6 Data exchange in S-MAC adaptive listen mode [30]. . . 17

2.7 Data exchange in T-MAC with TA [32]. . . 18

2.8 Data exchange in T-MAC with FRTS [32]. . . 18

2.9 Data gathering in D-MAC [36]. . . 19

2.10 Y-MAC frame format [40]. . . 21

2.11 Y-MAC channel hopping mechanism [40]. . . 21

2.12 Z-MAC channel-scheduling algorithm [41]. . . 22

2.13 Frame structure of ER-MAC [42]. . . 23

2.14 Buffer threshold setting in EE-Hybrid MAC based on the hop-count from the sink [43]. . . 24

2.15 M-PSM method to forward packets during a beacon interval [51]. . . 27

2.16 MH-PSM method to forward packets during a beacon interval [50]. . . 28

2.17 EAPSM management components [52]. . . 28

2.18 OPAMA example with an AP and one Station [53]. . . 30

2.19 SPEED architecture [59]. . . 32 xiii

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2.20 Architecture of the real-time scheme for (m,k)-firm streams for WSNs [64]. . . 35

2.21 Architecture diagram for RTLD [66]. . . 36

2.22 WVSN topology for AntSensNet with backbone outlined in black and

connect-ing the cluster heads [74]. . . 41

2.23 Greedy forwarding example for TPGF [81]. . . 44

2.24 PWDGR example for selecting pair-wise [83]. . . 45

2.25 Classification of the state-of-the-art energy efficient solutions. . . 46

3.1 The GREENNESS concept with the node scheduling mechanism running over

the LPR control channel illustrated by the arrows in orange. . . 52

3.2 GREENNESS Architecture. . . 53

3.3 Registration and Registration Acknowledgement messages used to collect the

WVSN active topology. . . 56

3.4 Typical LPR message format [84]. . . 64

4.1 Time Sequence Diagram for Chain Topology . . . 76

4.2 Regular network topologies . . . 78

4.3 Random Network Topology . . . 80

4.4 Network simulation with 30 WVSs randomly positioned in a 500 m x 500 m

area with the gateway identified with GW. . . 84

4.5 Testbed with a gateway and six WVSs using a Raspberry Pi model B boards as

basis for the GREENNESS proof-of-concept prototype. . . 86

4.6 The three regular WVSN topologies used to evaluate GREENNESS. . . 87

4.7 GREENNESS modification to the RDS message. . . 88

4.8 FM Tuner and connection with RPi. . . 89

4.9 Energy saving achieved by GREENNESS with respect to PACE for WVSNs

with different sizes and average number of hops. . . 91

4.10 Energy saving exhibited by GREENNESS when varying PLPRand considering

Wi-Fi radios in sleep mode. . . 92

4.11 Impact of different offered network loads and WVSNs sizes in the energy

saving of GREENNESS. . . 93

4.12 Network Capacity of GREENNESS and CSMA/CA for different offered

net-work loads with average number of hops equal to 2. . . 93

4.13 One-way-delay of GREENNESS and CSMA/CA for different offered network

loads with an average number of hops equal to 2. . . 94

4.14 Packet Loss Ratio for GREENNESS and CSMA/CA for different offered

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LIST OF FIGURES xv

4.15 Energy consumption of GREENNESS and PACE considering testbed,

simula-tion, and numerical evaluations for three scenarios . . . 96

4.16 Network capacity achieved by GREENNESS and PACE during testbed

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List of Tables

2.1 Comparison of out-of-band control solutions. . . 47

2.2 Comparison of MAC oriented solutions. . . 48

2.3 Comparison of routing oriented solutions. . . 49

3.1 Notations used in the description of the WATCM and NSM mechanisms. . . 54

3.2 Candidate Low Power Radios. . . 64

4.1 Notations used in the equations derived in the numerical analysis as well as in

the simulations, and experiments. . . 71

4.2 Simulation parameters. . . 83

4.3 Values of the parameters PLPR, Pidle, and PWiFisleep considered in the numerical

and simulations analysis. . . 84

4.4 Values of the measured parameters PLPR and Pidle. . . 90

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List of Acronyms

A-MSDU Aggregated MAC Service Data Unit

AC Access Category

ACK Acknowledgement

ACOWMSN Ant Colony Optimization-Based QoS Routing Algorithm

ADV-MAC Advertisement MAC

AGEM Adaptive Greedy-compass Energy-aware Multipath protocol

AM Amplitude Modulation

AntSensNet Ant-based multi-QoS routing metric

AODV Ad-hoc On-Demand Distance Vector

AP Access Point

ARP Address Resolution Protocol

ARQ Automatic Repeat Request

ASAR Ant-based Service-Aware Routing

ATCM Active Topology Creation and Maintenance

ATIM Ad-hoc Traffic Indication Map

ATIM-ACK ATIM-Acknowledgement

B-MAC Berkeley Media Access Control

BLE Bluetooth Low Energy

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CA Collision Avoidance

CAGR Compound Annual Growth Rate

CAQR Correlation-Aware QoS Routing

CBR Constant Bit Rate

CCA Clear Channel Assessment

COTS Commercial Off-The-Shelf

CRC Cyclic Redundancy Check

CSMA Carrier Sense Multiple Access

CSMA/CA Carrier Sense Multiple Access – Collision Avoidance

CTS Clear To Send

CW Contention Window

D-MAC Data–Gathering Medium Access Control

DARA Distributed Aggregate Routing Algorithm

DBP Distance-Based Priority

DCSIS Differential Coding-based Source and Intermediate nodes Selection

DD Directional Diffusion

DDPS Delay-differentiated Packet Scheduling

DGR Directional Geographic Routing

DMA Direct Memory Access

DSR Dynamic Source Routing

DSSS Direct Sequence Spread Spectrum

EA-TPGF Energy-Aware TPGF

EAPSM Energy Aware PSM

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List of Acronyms xxi

EDCA Enhanced Distributed Channel Access

EE-Hybrid MAC Energy Efficient Hybrid MAC

Eo11 Ethernet-over-802.11

EQSR Energy efficient and QoS aware multipath Routing

ER-MAC Emergency-MAC

ESSID Extended Service Set Identification

FCS Frame Check Sequence

FEC Forward Error Correction

FRM Failure Recovery Mechanism

FRTS Future-Request-To-Send

FSK Frequency-Shift Keying

GPIO General Purpose Input Output

GPS Global Positioning System

GPSR Greedy Perimeter Stateless Routing

GREENNESS Green wiReless vidEo sENsor NEtworks uSing out-of-band Signalling

GWR-MAC Generic WUR based MAC protocol

HTSMAC High Throughput Sensor MAC

IAR Improved Adaptive Routing

IBSS Independent Basic Service Set Identifier

IFS Inter-Frame Space

IoT Internet of Things

IP Internet Protocol

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ISM Industrial, Scientific and Medical

LEAR Load Based Energy-Aware Multimedia Routing

LECIM Low-Energy Critical Infrastructure Monitoring

LoRaWAN Long Range Wide Area Networking

LPL Low Power Listening

LPR Low Power Radio

LPWAN Low-Power Wide Area Networking

LSI Local Status Indicator

M-IAR Multimedia-Enabled Improved Adaptive Routing

M-PSM Modified PSM

MAC Medium Access Control

MAP Mesh Access Point

MCMP Multi-Constraint Multi-Path

MCRA Multi-Constrained Routing Algorithm

MCU Micro-Controller Unit

MH-PSM Multi-Hop PSM

MLME MAC Sublayer Management Entity

MMSPEED Multipath Multi Stateless Protocol for Real-Time Communication

MSN Maximum Slot Number

MTU Maximum Transmission Unit

NAV Network Allocation Vector

NFL Neighborhood Feedback Loop

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List of Acronyms xxiii

OEDSR Optimized Energy-Delay Subnetwork Routing

OPAMA Optimized Power save Algorithm for continuous Media Applications

OWD One-Way-Delay

PCA Priority Channel Access

PEMuR Energy efficient and perceived QoS aware video routing

PIR Pyroelectric Infrared

PLR Packet Loss Ratio

PRR Packet Reception Rate

PSM Power Saving Mode

PSNR Peak Signal to Noise Ratio

PWDGR Pairwise Directional Geographical Routing

PWM Pulse-Width Modulation

QEMAC QoS-supported Energy-efficient MAC

QMOR QoS aware Multi-sink Opportunistic Routing

QoE Quality of Experience

QoS Quality of Service

RDS Radio Data System

REAR Real-Time and Energy-Aware Routing

RFID Radio-Frequency Identification

RPAR Real-time Power-Aware Routing

RPi Raspberry Pi

RREQ Route REQuest

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RTLD Real-time routing protocol with load distribution

RTS Request To Send

RTS/CTS Request To Send/Clear To Send

S-MAC Sensor MAC

SAR Sequential Assignment Routing

SFD Start Frame Delimiter

SGF Selective Greedy Forwarding

SHPER Scalable Hierarchical Power Efficient Routing

SI Swarm Intelligence

SNGF Stateless Nondeterministic Geographic Forwarding

SNR Signal-to-Noise Ratio

SoBT Sleep on Beacon Transmission

SPEED Stateless Protocol for Real-Time Communication

SYNC Synchronisation

T-MAC Timeout MAC

TDMA Time Division Multiple Access

TIM Traffic Indication Map

TLS Time To Live

TPGF Two-Phase Geographic Greedy Forwarding

TR Topology Refresh

TxOP Transmit Opportunity

VBR Variable Bit Rate

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VoIP Voice-over-IP

WATCM WVSN Active Topology Collection Mechanism

WDS Wireless Distribution System

WiFIX Wi-Fi network Infrastructure eXtension

WMN Wireless Mesh Network

WMSN Wireless Multimedia Sensor Network

WSN Wireless Sensor Network

WUR Wake-Up Radio

WVS Wireless Video Sensor

WVSN Wireless Video Sensor Network

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Chapter 1 Introduction

1.1 Motivation

In 2019, Internet of Things (IoT) devices are expected to surpass the number of mobile phones and achieve the figure of 18 billion by 2022 [1]. IoT devices are foreseen to increase at a Compound Annual Growth Rate (CAGR) of 21 %, driven by new use cases such as connected cars, meters, wearables, industry, and agriculture. Fig. 1.1 presents the growth achieved so far and the forecast for connected devices with an Internet Protocol (IP) stack until 2022. While the number of laptops, tablets, mobile phones, and fixed phones connected has stopped growing, the number of IoT devices using unlicensed, short-range radios such as Wi-Fi and Bluetooth is growing exponentially and predicted to be around 16 billion by 2022 (bar in light green), clearly surpassing the other types of devices [1].

Figure 1.1: Forecast of connected devices with an IP stack [1].

On the other hand, the global wireless video surveillance market is growing at a CAGR of more than 20 %, driven by the shift from analogue to IP cameras, the low prices of cameras, and

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the increased security concerns [2]. IP cameras are affordable, flexible, scalable, and easy to install. Thus, the shift from analogue to digital cameras is facilitated, with many installations in stadiums, city surveillance projects, and hotels already available. With the emergence of cloud storage for the video streams generated, the overall costs of surveillance systems have been reduced, increasing the adoption by small businesses and residential market segments [2]. Because of political instability and terrorism in many regions around the world, stringent regulations have been approved to install security systems in public locations such as hospitals, airports, and railway stations. Notwithstanding, the growth of wireless cameras carries an energy demand to power-up these devices, either by connecting them directly to the power-grid or using batteries possibly recharged through renewable energy sources (e.g., solar panels).

The IT-services represent 2 % of all global carbon emissions, the same percentage of the emissions from the aviation sector [3]. The carbon footprint will grow since, by 2022,

the number of IoT devices will be six times the number of devices in 2016. Based on

these forecasts, governments, organisations, companies, and academia are discussing and investigating how energy consumption can be reduced. Within this context, a trend on "green networking" has emerged. Therefore, energy consumption optimisation of IoT devices, even for sensors without a battery, is important to control the expected growth of carbon emissions. For self-powered devices, e.g. Wireless Video Sensors (WVSs) running on batteries, the energy consumption becomes even more critical since it can affect the device regular operation. If the energy consumption is reduced, the battery can be smaller and the solution cost and carbon footprint lowered.

The outburst of connected low-cost devices, combined with the availability of affordable video cameras, is contributing to the emergence of Wireless Video Sensor Networks (WVSNs) as part of the IoT paradigm [4]. WVSNs enable a range of new applications in fields such as healthcare, agriculture, smart cities, intelligent transportation systems, and surveillance [5]. In these scenarios, there is usually the requirement of sending the video streams to a server located in the cloud [6][7]. The video stream should be transmitted reliably to the cloud with time constraints and minimal packet loss. Ethernet would be the candidate technology to fulfil these requirements but, for covering large areas for scenarios such as agriculture and environment monitoring, becomes expensive and not suitable. IEEE 802.11, also known as Wi-Fi (the two terms are used interchangeably in this thesis), is a suitable technology for transmitting video wirelessly, as it is ubiquitous and supports high data rates, especially the new variants IEEE 802.11ac [8] and IEEE 802.11ad [9]. For covering large areas, a single Access Point (AP) is not sufficient, so cameras need to relay information in a multi-hop topology to assure video

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1.2 Problem Statement 3

transmission to the cloud. Although other solutions like multiple APs interconnected using Wireless Distribution System (WDS) could be used to guarantee network coverage, this would increase the complexity of the solution. The installation becomes a complex and costly task since WDS requires a qualified technician to configure the virtual links between APs based on their radio coverage. Therefore, cameras equipped with an IEEE 802.11 radio and capable of communicating in multi-hop are envisioned as a suitable solution for these scenarios, as illustrated in Fig. 1.2.

Figure 1.2: Reference scenario for WVSNs using Wi-Fi cameras in multi-hop network topology and streaming video through a gateway to a cloud server.

1.2 Problem Statement

The transmission of video from WVSs through the gateway to the cloud has constraints regarding latency, packet delivery ratio, and throughput. IEEE 802.11-based WVSNs with multi-hop topologies and formed by WVSs supporting a single radio have three major prob-lems: low performance, throughput unfairness, and energy inefficiency [10]. In what follows, we detail each of them.

IEEE 802.11 uses the Carrier Sense Multiple Access – Collision Avoidance (CSMA/CA) mechanism to control the access to the medium. When used in multi-hop networks, this mechanism leads to low network performance, affecting latency and packet delivery ratio,

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(a) Hidden node problem. (b) Exposed node problem.

Figure 1.3: Hidden node and exposed node problems in IEEE 802.11-based multi-hop networks.

namely due to the hidden node problem [11]. As shown in Fig. 1.3a, when node A transmits a packet to node B and node C transmits a packet to node D at the same time, since node B is in the range of C, this generates a collision at node B. Since node C can not sense the transmission from node A, it can start a new data transmission to node B or D, originating a collision at node B. The Request To Send/Clear To Send (RTS/CTS) mechanism can solve the hidden node problem but creates the exposed node problem. In Fig. 1.3b, nodes C and D start to exchange data using RTS/CTS. Since node B and node E are in the transmission range, they receive the Request To Send (RTS) and Clear To Send (CTS) messages, respectively, and enter in the backoff period. During this period, node A and node F can send an RTS message, but nodes B and E do not reply due to the ongoing data transmission between nodes C and D, causing the exposed node problem. This affects the network throughput and latency.

The multi-hop nature of a WVSN brings up throughput unfairness since the nodes closer to the gateway tend to monopolise the medium, making the other nodes to starve [12]. In a multi-hop topology with every WVS sending video to the cloud, the WVSs closer to the gateway have the highest throughput, since their own traffic is enough to potentially fill up their queues, causing the packets from the farther WVSs to be dropped. This leads to the throughput unfairness problem.

The energy inefficiency problem is a consequence of 1) the collision of packets forcing their retransmission, 2) the overhearing of packets that are destined to other nodes, and 3) the idle listening since nodes must actively listen to the channel to receive packets. The collision of packets is caused by the low performance of CSMA/CA in multi-hop topologies, as explained above. Collisions imply retransmissions, thus wasting energy. Energy is lost in multi-hop topologies when a packet is overheard by several nodes in the same broadcast domain, forcing them to switch to receive mode and decode the packet even when that node is not the destination. WVS network interfaces must always be ON, even when not transmitting or receiving any packet, in order to forward packets from other nodes, forcing them to stay in

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1.3 Objectives 5

idle listening most of the time.

The transmission of video streams over IEEE 802.11-based WVSNs with multi-hop topolo-gies brings up challenges. Yet, by minimising collisions and only turning ON Wi-Fi radios when there is data to transmit/receive, network performance can be improved and energy can be saved. This is the approach followed in this thesis.

1.3 Objectives

The aim of this thesis is to develop a solution enabling green multi-hop WVSNs. The reference scenario is illustrated in Fig. 1.2. In this scenario, all the WVSs are equipped with cameras and send the video streams to the gateway using Wi-Fi, which in turn forwards them to the cloud server. The main objective is to minimise the energy consumption of WVSNs when transmitting video from each WVS to the cloud server. To attain the main objective, we consider the following specific objectives:

• Study related work about increasing energy efficiency in multi-hop scenarios, consider-ing the problems related to CSMA/CA presented in Section 1.2.

• Design an energy efficient solution that offers time guarantees and a high packet delivery ratio for wireless video sensing scenarios.

• Evaluate the proposed solution to determine the achievable energy consumption, perfor-mance, and throughput fairness for comparison with a CSMA/CA-based solution.

1.4 Original Contributions

The main contribution of this thesis is the Green wiReless vidEo sENsor NEtworks uSing out-of-band Signalling (GREENNESS) solution. Featuring a low power out-of-band control channel and a traffic-aware Node Scheduling Mechanism (NSM), it enables significant energy savings while improving network capacity and throughput fairness when compared to CSMA/CA-based WVSNs. The GREENNESS solution includes the following specific contributions:

• GREENNESS concept and architecture. GREENNESS combines a node polling mechanism, such as the one defined in [13], with the use of out-of-band signalling over a Low Power Radio (LPR) integrated into each WVS node. Fig. 1.4 presents the

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GREENNESS concept, considering a multi-hop WVSN with an LPR installed in each WVS and the gateway.

LPR range

C WVS# 1 C C C C C Wi-Fi Link Gateway LPR WVS# 1 WVS#2 WVS# 3 WVS#4 WVS#5 WVS#6 mac₃ mac₄ mac₅ mac₆

mac₂ mac₁

LPR Link

Figure 1.4: The GREENNESS concept, with the node scheduling mechanism running over the LPR control channel illustrated by the arrows in orange.

• WVSN Active Topology Collection Mechanism (WATCM). The WATCM mechanism finds the network topology and computes the optimum polling order for the NSM

mechanism. The polling order is calculated to minimise the number of times the

Wi-Fi radio changes between ON/OFF states, as a higher number of changes affects the performance. The control messages overhead are also optimised by controlling the relay nodes and WVSs with a single Poll message.

• Node Scheduling Mechanism (NSM). The NSM mechanism uses the LPR included in the gateway to schedule the WVS data transmissions and turn the WVS Wi-Fi radios ON/OFF accordingly. By including a traffic-aware mechanism, energy efficiency is further improved. When there is no traffic, a WVS is not polled, so the Wi-Fi radio is kept OFF. The traffic-aware mechanism learns the traffic pattern for Constant Bit Rate (CBR) flows and changes the WVSs polling order accordingly.

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1.5 Publications 7

• Failure Recovery Mechanism (FRM). The FRM mechanism runs when the network topology changes. When the topology changes or a node is disconnected from the network, a failure is detected and WVSs are forced to turn ON their Wi-Fi interfaces. This allows the routing algorithm to run and the WATCM to find the new WVSN topology.

GREENNESS differs from related work by supporting multi-hop networks and minimising signalling overhead, without changing the current IP stack. Furthermore, it addresses the low performance, throughput unfairness, and energy inefficiency that affect WVSNs in a holistic way.

1.5 Publications

1.5.1 Journals

• F. Sousa, J. Dias, F. Ribeiro, R. Campos, and M. Ricardo, “Green Wireless Video Sensor Networks Using Low Power Out-of-Band Signalling”, IEEE Access, vol. 6, pp. 30024–30038, Jun. 2018.

• R. Campos, R. Duarte, F. Sousa, M. Ricardo, and J. Ruela, “Network Infrastructure Extension Using 802.1D-based Wireless Mesh Networks”, Wireless Communications and Mobile Computing, vol. 11, no. 1, pp. 67–89, Jan. 2011.

1.5.2 Conferences

• F. Sousa, J. Dias, F. Ribeiro, R. Campos, and M. Ricardo, “A Traffic-aware Solution for Green Wireless Video Sensor Networks”, in Proc. of IFIP/IEEE Wireless Days 2017, Porto, Portugal, Mar. 2017.

• J. Dias, F. Sousa, F. Ribeiro, R. Campos, and M. Ricardo, “Green Wireless Video Sensor Networks using FM Radio System as Control Channel”, in Proc. of WONS 2016, Cortina d’Ampezzo, Italy, Jan. 2016.

• F. Sousa, R. Campos, and M. Ricardo, “Energy-efficient Wireless Multimedia Sensor Networks using FM as a Control Channel”, in Proc. of the 9th IEEE Symposium on Computers and Communications (ISCC’14), Funchal, Portugal, Jun. 2014.

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1.5.3 Workshops and Talks

• F. Sousa, J. Dias, F. Ribeiro, R. Campos, and M. Ricardo, “A Traffic-aware Solution

for Green Wireless Video Sensor Networks”, 23o RTCM Seminar, Aveiro, Portugal,

Jul. 2017.

• F. Sousa, R. Campos, and M. Ricardo, “Energy-efficient Wireless Multimedia Sensor Networks using FM as a Control Channel”, in MAP-Tele Workshop, Porto, Portugal, May 2016.

• F. Sousa, R. Campos, and M. Ricardo, “Energy-efficient Wireless Multimedia Sensor Networks using FM as a Control Channel”, in MAP-Tele Workshop, Aveiro, Portugal, June 2014.

• F. Sousa, F. Abrantes, and M. Ricardo, ‘Cooperation Between WPAN and WLAN Nodes For Efficient And Interoperable Communication”, in MAP-Tele Workshop, Guimarães, Portugal, May 2012.

• F. Sousa, F. Abrantes, and M. Ricardo, ‘Cooperation Between WPAN and WLAN Nodes For Efficient And Interoperable Communication”, in MAP-Tele Workshop, Aveiro, Portugal, May 2011.

1.6 Document Structure

The structure of this PhD thesis is as follows. Chapter 2 presents the related work on green wireless video sensors networks. Chapter 3 describes the GREENNESS solution, namely the traffic-aware node scheduling mechanism and the candidate wireless technologies for imple-menting the low-power control channel. Chapter 4 presents the evaluation of the GREENNESS solution considering numerical, simulations, and experimental analysis. Chapter 5 reviews this PhD thesis work, draws the main conclusions, recalls the main contributions of the PhD thesis, and points out the future work.

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Chapter 2 State-of-the-Art in Energy-Efficient

Solutions

IEEE 802.11-based Wireless Video Sensor Networks (WVSNs) with multi-hop topologies and formed by Wireless Video Sensors (WVSs) supporting a single radio have three major problems: low-performance, throughput unfairness, and energy inefficiency. A number of solutions have been proposed in the literature to address these problems. In the state-of-the-art survey provided in this chapter, we present energy efficient solutions, offering performance guarantees for the targeted wireless video sensing scenario.

In this chapter, we classify the state-of-the-art solutions in three types: 1) out-of-band control oriented; 2) MAC oriented; 3) routing oriented. The out-of-band control oriented solutions are divided in two sub-categories: solutions using a Wake-Up Radio (WUR)-receiver

and solutions using a WUR-transceiver. The MAC oriented type is further divided into

contention based, hybrid, and Power Saving Mode (PSM) based. The routing oriented type is further classified according to the following aspects: QoS constraints, “Swarm Intelligence (SI) based” routing, and network structure.

2.1 Out-of-Band Control Oriented Solutions

In order to improve the energy efficiency in WVSNs, it is necessary to reduce the idle listening of the Wi-Fi interface. A possible solution to increase the energy efficiency is to use an out-of-band control channel to reduce the idle listening of Wi-Fi interfaces while keeping latency low. To achieve this, a WUR is added to the WVS so that it can continuously listen to the control channel, as shown in Fig. 2.1. When the WUR receives a wake-up signal, the

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Figure 2.1: Wireless Video Sensor Architecture with Wake-Up Radio [15].

Controller Unit (MCU) and the main radio are awake. The literature defines three different schemes to use WURs [14]:

WVS with WUR-receiver the source WVS sends a wake-up signal and the destination WVS receives it through the WUR-receiver, which wakes-up the Main transceiver of the destination WVS. Afterwards, an acknowledgement message is sent to the source WVS and the transmission of data to the destination WVS is started. This is illustrated in Fig. 2.2a.

WVS with WUR-transceiver where the source WVS sends a wake-up signal through its WUR-transceiver and the destination WVS receives it through its WUR-transceiver. The Main transceiver of the destination WVS is woken up, and an acknowledgement message is sent to the source WVS using the WUR-transceiver. The source WVS can then transmit the data to the destination WVS using the Main transceiver. This is shown in Fig. 2.2b.

WVS with WUR-transceiver only the source WVS sends the wake-up signal and data to the destination WVS, using the WUR transceiver. The destination WVS receives such information through the WUR-transceiver and sends an acknowledgement message to the source WVS. There is no Main transceiver in this case. This is represented in Fig. 2.2c.

In these three schemes, the WUR-transceiver comprises low-power transmitting and re-ceiving circuits, while the WUR-receiver includes the rere-ceiving circuit only. A WVS with the receiver only implies unidirectional communications, while a WVS with the WUR-transceiver ensures bidirectional communications. The scheme using the WUR-WUR-transceiver

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2.1 Out-of-Band Control Oriented Solutions 11

only can be used to wake-up a transponder, as proposed in [16]. The proposed solution consists of a low power radio device which is typically in sleep mode and can be woken up by an event and moved to the active state. Since our objective is to stream video from several WVSs, a WUR-transceiver only cannot be adopted because of its low data rate. Thus, the valid schemes when it comes to the use of out-of-band control channel are WVS with WUR-receiver and WVS with WUR-transceiver.

(a) WUR-receiver waking up the main transceiver after wake-up signal

(b) WUR-transceiver receiving and transmitting a wake-up signal

(c) WUR-transceiver receiving and transmitting both wake-up signal and data.

Figure 2.2: Wake-Up Radio communication schemes [14].

When the WUR-receiver scheme is used, a central node uses the WUR for sending out-of-band signalling and for controlling the access to the medium of the WVSs that carry a WUR-receiver [17]. In the WUR-transceiver scheme, the control is delegated to a WVS which performs simple tasks of gathering information from the surrounding environment and can also

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trigger the access to the medium of WVSs. This scenario is used when the WVSN adopts a multi-tier architecture [18]. In the following subsections, we refer to state-of-the-art solutions exploring these two schemes.

2.1.1 Solutions Adopting the Wake-Up Radio Receiver Scheme

Tang et al [17] present a solution for energy and spectrum efficiency by tightly integrating a low-power WUR with a WLAN module, which is only used for the data transmission/reception. The WUR is used for carrier sense, contention control, and remote/local wake-up. Fig. 2.3 shows the low power WUR that is kept awake to monitor the channel and the WLAN module put in sleep mode when the channel is idle. Before sending a packet, the WUR performs carrier sense and backoff (BO). A packet is sent when the backoff counter reaches 0, and the WLAN module wakes up (WuR-CS). Furthermore, the Contention Window (CW) is adjusted based on the length of Inter-Frame Space (IFS) measured by the WUR and the estimation of the number of contention slots for each transmission (WuR-CSMA). Compared with CSMA, WuR-CSMA reduces the power consumption by more than 90 % in the saturation case, when the traffic rate is 5000 packet/s. Nevertheless, WuR-CS and WuR-CSMA were designed for Wi-Fi networks running in infrastructure mode and not the multi-hop scenarios targeted in this thesis.

Figure 2.3: Tang et al model of a mobile node [17].

RT-Link is a time synchronised real-time sensor networking platform proposed by Rowe

et al[19] that uses an Amplitude Modulation (AM) signal to synchronise the network nodes

globally. This platform employs a Time Division Multiple Access (TDMA) scheme where the nodes go to sleep and wake up during their transmission time slot. RT-Link was designed for

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scenarios that require throughput and latency guarantees, together with energy efficiency. RT-Link outperforms Berkeley Media Access Control (B-MAC) regarding packet collisions and end-to-end delay. Nevertheless, RT-Link was not tested for wireless video sensing scenarios and does not provide throughput fairness.

In [20] a radio-triggered circuit is used to switch the environmental sensors between wake-up and sleep modes; when a sensor node is in the sleep mode, all its components are shut down, except the memory, the interrupt handler, and the timer. A radio signal can power-up the radio-triggered circuit and change nodes’ state to wake-up mode. This solution employs a multiple-frequency technique by using a Radio – Triggered ID (RTID) to improve the selectivity of sensors that should be in wake-up mode. The selectivity of the solution is reduced because it selects more nodes than needed to transmit information and uses multiple radios and frequencies. The poor selectivity reduces the energy savings. Moreover, RTID does not offer any Quality of Service (QoS) guarantees.

A working prototype for a WUR is presented by Doom et al [21], which was designed with standard components and reuses as much as possible the primary radio (CC1000) of the T-node to operate in the 868 MHz band. The wake-up signals are generated by software that toggles the transmitting power of the CC1000 radio between the minimum and maximum values. The receiving circuit is dedicated to filter out interference and retrieve the wake-up signal. Still, the wake-up radio is not designed with multi-hop topologies in mind and only addresses the energy inefficiency problem.

The solutions described in [22] and [23], despite being proposed for very different sce-narios, are also based on the concept of shutting down or entering in low power state when a sensor or device is in idle state. In [22] a TinyNode 184 [24] is used to carry out-of-band control information between a terminal and the Access Point (AP), in order to maintain connectivity and wake up the AP when necessary. Whenever the terminal starts a new session – e.g., Internet browsing or Voice-over-IP (VoIP) – it sends a beacon through the TinyNode 184 to wake up the AP. The beacon carries a Time To Live (TLS) field that indicates the amount of time the AP Wi-Fi interface must be turned ON. Using this scheme, the authors estimate that, for an average usage pattern of 4 hours of active Internet usage per day, the total real power savings are 23 %. Nevertheless, this solution was designed for Wi-Fi networks in infrastructure mode and does not support multi-hop wireless topologies. Furthermore, it only addresses the energy inefficiency problem. In [23] the authors adopt a similar scheme to increase the battery lifetime of a PDA-based phone by reducing its idle power consumption. To achieve this, the wireless network card of the PDA-based phone is shut down when the device is not in use. The device

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is powered when an incoming call is received through a MiniBrick, which is a low-power out-of-band signalling radio added to the PDA-based phone. Although the authors claim that they double the battery lifetime, the solution was only designed for single-hop scenarios.

2.1.2 Solutions Adopting the Wake-Up Radio Transceiver Scheme

The patent presented in [25] describes an implementation to reduce the battery consumption of an energy-constrained computing device, by selecting between a low-power radio (low data rate with a low power consumption) and a high-power radio (high data-rate with high power consumption) to minimise power consumption while keeping effective wireless data communications. The dual-radio communications system switches from the low-power radio to the high-power radio when the user demands high data-rate, the low-power radio is congested, or the data generated by the low-power radio exceeds a predefined threshold. This patent addresses the energy efficiency problem for single networks but does not discuss multi-hop scenarios and the performance and throughput unfairness problems.

Figure 2.4: SleepyCAM power management solution [26].

Mekonnen et al propose the sleepyCAM power management solution [26][27] for wireless video sensing scenarios, which is illustrated in Fig. 2.4. SleepyCAM uses a Pyroelectric Infrared (PIR) sensor to detect movement and uses an ATmega1281 to control the power supply of the camera node composed of a Raspberry Pi (RPi) and a camera module. This solution evolved to a multi-tier Wireless Sensor Network (WSN) with motion sensors in tier 1, connected through Bluetooth Low Energy (BLE) radios and a Wi-Fi network in tier 2,

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Figure 2.5: Mekonnen et al Multi-Tier Architecture [18].

connecting the RPi-Camera nodes [18], as shown in Fig. 2.5. PIR motion sensors, using BLE radios, activate the streaming from the RPi-Camera nodes to a remote PC when motion is detected. Although Mekonnen et al proposed solution addresses a video streaming scenario, it relies on motion to activate the streaming and does not solve the low-performance problem when movement is detected in the range of all cameras, and multiple streams are sent to the cloud.

A two-tier strategy for priority based critical event surveillance with wireless multimedia sensors was proposed by Bhatt et al [28]. The authors developed a two-tier architecture with audio nodes densely deployed and video nodes sparsely deployed. Audio sensors work continuously to capture events and send this information to the base station which wakes up the video nodes in the region of interest. According to the authors, this approach is energy efficient and has a low deployment cost, but it cannot be applied to the wireless video sensing targeted scenarios since audio and video nodes coexist in a WVS.

The Generic WUR based MAC protocol (GWR-MAC) was proposed in [15] to improve energy efficiency by avoiding idle listening. Each node has two radios that are not restricted to any WUR technology. The wake-up procedure is bidirectional and can be source-initiated or sink-initiated. In the source-initiated mode, the sensor nodes send a wake-up signal to the sink node. When the signal is received, the main radio of the sink node is turned ON and a beacon message is broadcasted to initiate the transmission period for the sensor nodes. In the sink-initiated mode, the sink node wakes up the other sensor nodes from the sleep mode. When the sensor nodes receive the wake-up signal, the Main transceiver is turned ON, and an acknowledgement message is sent to the sink nodes through the WUR. Afterwards, the sink node sends a beacon message containing information about the following transmission

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period to the sensor nodes. Nonetheless, GWR-MAC was not designed for video streaming scenarios since the energy consumption only decreases significantly when the number of events transmitted per hour is low [15].

2.2 MAC Oriented Solutions

As mentioned in Chapter 1, most of the problems associated with IEEE 802.11-based WVSN are caused by the Medium Access Control (MAC) mechanism used. One alternative is to have sleep periods that are controlled by a duty cycle-based MAC protocol when incoming transmissions are not detected. Duty cycle based MAC protocols are suitable for applications with low traffic load since by lowering the duty cycle this will increase the communication delay. Therefore, this section presents MAC oriented solutions, including contention-based, hybrid, and power saving solutions, that have been proposed in the state-of-the-art to tackle the three problems stated in Chapter 1.

2.2.1 Contention-Based

In contention-based MAC protocols, the WVS contend for accessing the media and colli-sions are avoided through probabilistic coordination.

Sensor MAC (S-MAC) is a contention-based protocol that reduces idle listening by peri-odically putting nodes into sleep state to save energy [29]. To attain this objective, S-MAC nodes change between the listen and sleep states in a duty-cycle. In the sleep state, each node turns OFF its radio and sets a timer to change to the listen state. Since S-MAC requires the receiver and the sender to be simultaneous in the listening state, they need to be periodically synchronised to avoid node’s clock drift. Besides, nodes share their own sleep schedules by broadcasting them to their neighbours. S-MAC adaptive listening was proposed in [30] to improve the latency in a multi-hop network caused by the periodic sleep state. For a multi-hop network, neighbouring nodes need to wait for the next listening period to transmit data, which increases the end-to-end latency. To address this issue, during an adaptive listen period nodes can overhear a neighbour’s transmission – e.g., Request To Send (RTS) and Clear To Send (CTS) – to learn its duration and adaptively wake up when the transmission is over. Fig. 2.6 presents a timing diagram with this sequence. When the next-hop node is a neighbour of the sender, it receives the RTS, or in the case it is a receiver, it receives the CTS. In either case, the neighbours learn the transmission duration and can schedule the wake-up period to reduce latency. S-MAC outperforms IEEE 802.11 for light offered traffic. However, for

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2.2 MAC Oriented Solutions 17

video transmitting scenarios, S-MAC consumes more energy than IEEE 802.11 because of the overhead it uses with Synchronisation (SYNC) packets [31].

Figure 2.6: Data exchange in S-MAC adaptive listen mode [30].

Since the listening period duration of S-MAC is fixed, when the duration is too large this results in a waste of energy; when it is too small, data loss occurs. To overcome the fixed listening period, Timeout MAC (T-MAC) was proposed. It uses an active period that adapts to the traffic pattern [32]. Fig. 2.7 shows that during the active time T-MAC sensors send data and wait for a time TA. When no communication is observed, they return to the low power sleep mode to minimise the idle listening. The TA has to be long enough so that any neighbour can hear a potential CTS frame. In multi-hop networks, T-MAC suffers from the early sleeping problem, caused by a node not hearing the CTS message. Fig. 2.8 exemplifies this problem. Node A sends an RTS message and node B sends back a CTS message. Although node C can still hear node’s B CTS message, node D cannot and returns to sleep mode after TA is over. The Future-Request-To-Send (FRTS) technique was defined to address the problem. The FRTS message is sent when a CTS message is overheard by node C, as shown in Fig. 2.8. When node D receives the FRTS message, it keeps active, waiting to receive messages from node C. The FRTS technique increases the throughput by 75 % [32]. For high traffic loads, both S-MAC and T-MAC are inefficient since the exchanged messages are concentrated in a short time. Moreover, in T-MAC the FRTS technique increases the idle listening times and therefore the energy consumption of nodes.

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Figure 2.7: Data exchange in T-MAC with TA [32].

Figure 2.8: Data exchange in T-MAC with FRTS [32].

Another alternative to achieve low power consumption is the B-MAC, which reduces the duty cycle and idle listening by implementing an adaptive preamble sampling scheme [33]. The low power consumption is attained by reducing the radio’s duty cycle and idle listening through a periodic channel sampling named Low Power Listening (LPL). This mechanism is similar to preamble sampling in Aloha [34] but was designed for different radio characteristics. The node powers up when it detects an incoming packet and stays awake enough time to process the packet and return to sleep mode. B-MAC uses noise floor estimation for finding a clear channel to transmit data but also to find if the channel is active during LPL. To avoid collisions, B-MAC uses Clear Channel Assessment (CCA) together with backoff mechanisms for reliable link layer acknowledgements. B-MAC was designed for applications that focus on energy efficiency and outperforms S-MAC, but it is not fair with respect to packet delivery ratios.

Data–Gathering Medium Access Control (D-MAC) was designed and optimised for data gathering trees in WSNs, i.e., sequentially wake-up nodes in a multi-hop path to forward data across different nodes until it reaches the gateway, as shown in Fig. 2.9 [35]. D-MAC divides

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the node’s schedule in three modes: receiving, sending, and sleeping. In Fig. 2.9 the tree leaf node starts in receiving mode, but the neighbour does not have data to send. Afterwards, the leaf node switches to sending mode and sends a packet to the upstream neighbour, which was already in receiving mode and sends back an Acknowledgement (ACK) message. The leaf node can now turn OFF its radio and change to sleeping mode. The node can request more slots if it needs to transmit more data. The receiving and sending periods are fixed and identical, being enough to transmit and receive one packet. When the depth of the tree is known, the nodes can set their wake schedule ahead from the gateway, and periodically move between the three states. D-MAC decreases the latency and energy consumption for tree-based multi-hop topologies and can adapt the duty cycle to the traffic variation. Nevertheless, D-MAC does not consider node fairness, and interference between nodes in the same depth level is handled through further protocol overhead.

Figure 2.9: Data gathering in D-MAC [36].

2.2.2 Hybrid

A hybrid MAC protocol is the combination of a TDMA with Carrier Sense Multiple Access (CSMA). TDMA has the advantage of being suitable for high traffic scenarios since it avoids collisions, but it requires global synchronisation, it does not adapt to topology changes, and hardly discovers interference amongst neighbour nodes. On the other hand, CSMA provides the best result for low traffic scenarios, but it experiences problems with the hidden terminal, which can cause more packet collisions. During the data period, only the intended receivers are awake, and the other nodes are in a low power sleep mode.

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Advertisement MAC (ADV-MAC) is a hybrid MAC protocol similar to S-MAC and T-MAC, but it uses an advertisement message for intended receivers to minimise the energy lost in idle listening while keeping throughput and latency [37]. ADV-MAC is composed of four different periods: synchronisation, advertisement, data, and sleep. The synchronisation period works as S-MAC and T-MAC. Next, there is a fixed advertisement period that the nodes use to transmit advertisement packets containing an ID of the intended receivers. Nodes that do not participate in the data transmission switch to the sleep period. The ADV-MAC energy consumption is 30 % less when compared with T-MAC and 41 % when compared with S-MAC with a 20 % duty cycle while improving throughput and latency [37]. Nonetheless, ADV-MAC has two significant disadvantages [31]: advertisement packets are not confirmed, and in the case of collisions the intended receiver will be in sleep mode while the transmitting node is awake, thus wasting energy; when many nodes assign slots in the advertisement period, the data period is not enough to assure data transmission for all nodes. Therefore, ADV-MAC cannot guarantee packet loss ratio and throughput fairness for a wireless video sensing scenario.

X-MAC is an asynchronous MAC protocol that uses short preambles, embedding the target ID of the receiver, instead of one long preamble, as in B-MAC [38]. When the receiver node wakes up, it checks the ID on the preamble packet. If it is not the intended recipient, the node returns to sleep, continuing its duty cycle. Otherwise, it sends an ACK message and remains awake to receive the data packet. Since X-MAC avoids the synchronisation overhead, it is more energy efficient than S-MAC. Moreover, experiments demonstrate that X-MAC achieves significant gains over LPL implementations when it comes to energy consumption, latency, and throughput. Furthermore, the performance gains of X-MAC continually increase with the density of the network. In [39] X-MAC was improved with a collision avoidance algorithm named X-MAC/CA. When combining X-MAC with a Collision Avoidance (CA) mechanism, the transmissions are randomised in the overcrowded network, thus reducing the probability of collisions. X-MAC/CA improves the throughput in 30 % when compared to X-MAC [39], but cannot offer packet loss ratio and throughput fairness guarantees.

Y-MAC combines TDMA and CSMA protocols with light-weight hopping mechanism to achieve both high performance and energy efficiency under high-traffic conditions [40]. Since Y-MAC is a TDMA-based protocol, the frame is divided into fixed-length time slots, with each frame composed of broadcast and unicast periods, as shown in Fig. 2.10. The number of time slots in each frame can be increased to allocate exclusive time slots to more nodes, but latency shall also increase due to the extended length of the frame period. Nevertheless, Y-MAC proposes the use of multiple channels, as an alternative to increasing the number of possible

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time slots. Since multiple channels are used, sensor nodes require time synchronisation. A simple synchronisation technique is included to synchronise the upcoming timer events of nodes by adjusting the expiration times of these events. After a predefined time, if nodes do not receive any control message, they are considered detached from the network and are moved to sleep mode. Under light traffic conditions, this scheme is energy efficient because nodes access the medium only during the broadcast and unicast receive time slots. For high traffic levels, nodes have to wait for a unicast time slot, and messages have to wait in a queue, thus increasing latency.

Figure 2.10: Y-MAC frame format [40].

Figure 2.11: Y-MAC channel hopping mechanism [40].

Y-MAC overcomes this problem by implementing a light-weight channel hopping mecha-nism that uses multiple channels to reduce latency. This mechamecha-nism is presented in Fig. 2.11,

assuming that four channels are available and f1 is the base channel. When a node receives

a message in the f1channel, it hops to the next channel using a hopping sequence generation

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receiver it also hops to the same channel and competes to access the medium in the contention window. The contention winner is penalised by limiting the range of its back-off timer value for the next transmission. This way per node fairness is guaranteed. In Y-MAC the overhearing problem is reduced since receive time slots are allocated to nodes; thus, Y-MAC maintains a low energy consumption while achieving a high delivery rate of bursty messages under high traffic conditions. Nonetheless, Y-MAC cannot guarantee a packet loss ratio and throughput fairness for offered loads typical in wireless video sensing scenarios.

Figure 2.12: Z-MAC channel-scheduling algorithm [41].

Like in Y-MAC, Zebra MAC (Z-MAC) combines the strengths of CSMA and TDMA mechanisms, by adapting itself to the level of contention in the network [41]. Z-MAC was designed to behave like CSMA under low contention and switch to TDMA under high con-tention. Furthermore, by combining both mechanisms, Z-MAC becomes better than a stand-alone TDMA with respect to the robustness to topology changes, time synchronisation failures,

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time-varying channel conditions, and slot assignment failures. Z-MAC reuses channels by adopting a Distributed RAND [41], an efficient, scalable channel-scheduling algorithm, which allocates slots for all the nodes in the network. A slot is periodically assigned to a node, and each node can reuse its slot. Two-hop neighbours of a node can get the same slot since DRAND allows any two nodes beyond their two-hop neighbourhoods to own the same slot. Before allocating time slots to nodes, Z-MAC runs a neighbour discovery protocol, which broadcasts a ping to its one-hop neighbours. Using this protocol, each node collects the information from its one-hop neighbours, which constitutes the two-hop neighbours. This two-hop neighbour list is used as input to the DRAND algorithm. After allocating a time slot, each node needs to decide the time frame of the node, i.e., the period a node can use the time slot for transmission. To calculate the time frame each node propagates the Maximum Slot Number (MSN) to its neighbours. Fig. 2.12 shows on the top the network topology with the slot numbers assigned to each node; the numbers in parenthesis are the MSN. The bottom part of Fig. 2.12 shows the slots schedule for all nodes. The dark slots represent "empty" slots; the shaded slots represent time slot that can be used to transmit. Besides the neighbour discovery, slots assignment, and local framing, Z-MAC requires local synchronisation since this protocol only involves one-hop and two-one-hop neighbours. After the setup phase, the nodes forward the frame size, the slot number to two-hop neighbours, and maintain synchronisation. Z-MAC is more efficient at high contention levels, showing 40 % higher fairness index than B-MAC. It is energy inefficient for low contention levels and does not offer packet loss ratio and throughput guarantees.

Figure 2.13: Frame structure of ER-MAC [42].

Emergency-MAC (ER-MAC) was designed for fire monitoring in building scenarios but can also be useful for other WSN emergency applications [42]. For high volumes of traffic, ER-MAC allows contention in TDMA time slots, trading energy efficiency for higher packet delivery ratio and lower latency. Furthermore, ER-MAC was designed with two priority queues to distinguish high priority packets for emergency scenarios, from low priority data, i.e., non-critical data. When the high priority queue is empty, the non-non-critical data is sent from the low priority queue. Nodes enter a low power sleep mode when there is no data to be transmitted.

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The ER-MAC frame structure, as shown in Fig. 2.13, is composed of contention-free slots

with ts duration and a contention period with tc duration. Similarly to Z-MAC, a

contention-free slot is assigned to more than one node within the two-hop neighbourhood. In case of

emergency, each contention-free slot is further divided into sub-slots (t0, t1, t2, t3). The

contention period at the end of the frame is used to add new nodes. In [42], simulation results prove that ER-MAC has higher packet delivery ratio, lower latency, and lower power consumption when compared with Z-MAC. ER-MAC consumes more energy in normal mode than in emergency mode and does not guarantee a packet loss ratio and throughput fairness.

Figure 2.14: Buffer threshold setting in EE-Hybrid MAC based on the hop-count from the sink [43].

For industrial monitoring scenarios, Pandeeswaran et al [44] developed Energy Efficient Hybrid MAC (EE-Hybrid MAC), that aims to be a low latency MAC protocol, assuring high packet delivery ratio and energy efficiency. Like Z-MAC, EE-Hybrid MAC is a hybrid protocol combining the best features of TDMA and CSMA depending on the traffic pattern. The EE-Hybrid MAC was designed based on Z-MAC, but it includes a priority region and changes the buffer memory level depending on the node distance to the sink node. When a node from a high priority region sends data to its neighbours, it identifies the data as high priority, switches to TDMA mode, and allocates the first slot to that node. If more than one node is in the high priority region, adjacent slots are allocated by neighbours. To assure energy efficiency, the total memory to store information in the node before forwarding it to neighbours can also

be adjusted, as presented in Fig. 2.14. The buffer threshold Qthreshold is set up based on the

hop-count from the sink to the node. Nodes closer to the sink will get a higher threshold; the threshold diminishes with the hop-count. Nodes are continuously in sleep mode. They only move to active mode if the number of arriving packets exceeds the buffer threshold [43]. EE-Hybrid MAC provides better results for packet delivery ratio, and insignificant energy

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consumption reduction when compared to S-MAC, Z-MAC, and T-MAC. Nevertheless, in the end-to-end delay results, T-MAC outperforms EE-Hybrid MAC, and EE-Hybrid MAC does not offer throughput fairness.

In [45] the authors have proposed the High Throughput Sensor MAC (HTSMAC) for surveillance applications. The streaming of images is triggered when a sensor node detects high temperature, thus indicating a possible fire hazard in the zone. HTSMAC improves MAC and makes the protocol switch between two operation modes: normal mode – using S-MAC for WSN nodes to sense temperature, humidity, and luminance; image mode – using

RIPPLE, sensor nodes power ON the camera and send images to the sink. The SYNC

packets of S-MAC are used to switch between normal and image modes. In our targeted scenarios, the cameras are continuously transmitting video, so there is no need to switch between modes. Moreover, Ripple protocol was designed for multi-hop network multimedia applications without considering power efficiency.

QoS-supported Energy-efficient MAC (QEMAC) improves the throughput fairness and energy-efficiency of the standard IEEE 802.11e for scenarios of video surveillance, locali-sation, telemedicine, and industrial processes [46]. While the QoS support is based on an improved version of IEEE 802.11e, energy-efficiency is achieved by employing a dynamic duty cycling sleep/awake mechanism. QEMAC provides rate fairness by assuring that no internal collisions occur by assigning the Transmit Opportunity (TxOP) to each Access Category (AC). The sleep/awake mechanism is based on the Request To Send/Clear To Send (RTS/CTS) exchange, which can happen multiple times in a single TxOP. The QEMAC’s main drawback is the support of single-hop networks only and the periodic wake up of nodes to receive RTS frames.

In [13] PACE was proposed as an evolution to Wi-Fi network Infrastructure eXtension (WiFIX). WiFIX is a simple and efficient tree-based routing solution overlaid on the 802.11 MAC. It configures an active tree topology rooted at the gateway and uses 802.1D bridges and their simple learning mechanism for frame forwarding. Still, it also suffers from performance inefficiency and throughput unfairness due to the use of the Carrier Sense Multiple Access – Collision Avoidance (CSMA/CA). PACE enables coordinated access among nodes to prevent collisions, without requiring explicit synchronisation and fixed packet size. PACE assumes that a logical tree topology, rooted at the gateway, is configured over the physical network using WiFIX. In PACE, the gateway controls the access to the medium by limiting transmissions to a single node at each time. Thus, each node can transmit a packet in each network-wide transmission round, ensuring performance and throughput fairness. The packet received by the

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destination node is implicitly used as a token that grants the permission to send a packet. When the gateway receives the packet coming from the destination node, the same process is repeated with another destination node, until all the nodes have had the opportunity to transmit one packet and the first destination node can be authorised to send again. Since control packets can degrade performance [13], PACE exchanges packets in both directions, whenever possible, by using a flag in the poll message to indicate whether the gateway has a packet to be transmitted to a node. An explicit control packet is sent by the gateway when there is no data to transmit. Additional signalling is embedded in data packets to minimise the control overhead. In multi-hop networks, PACE default is to send one packet per poll to avoid intra-flow interference and performance degradation by relay nodes. PACE outperforms CSMA/CA-based solutions for near-saturation or saturated Wireless Mesh Networks (WMNs) regarding goodput, delay, and fairness [13]. In non-saturated WMNs, PACE’s delay can be higher than in CSMA/CA-based networks since sending an explicit poll control message has a high cost. Nevertheless, PACE was not designed to be energy efficient and wastes resources when polling signals are not embedded in data frames.

2.2.3 Power Saving Mode

IEEE 802.11 standard proposed an amendment [47], which introduces PSM to increase the lifetime of IEEE 802.11 stations running on battery, such as smartphones. PSM was firstly designed for single hop networks running in infrastructure mode, so it performs poorly for ad-hoc mode, especially in multi-hop networks [48][49]. PSM increases the packet delay when a data frame is forwarded across multi-hop networks since nodes on subsequent hops stay in the doze state until a traffic announcement is received. On each hop, the frame waits for the beacon interval, before being forwarded, and for a high number of hop-count the end-to-end delay increases, affecting time-sensitive applications. Moreover, nodes are forced to stay awake to respond to probe requests from nodes that are scanning the medium for joining the network. For instance, in an IEEE 802.11 ad-hoc network with two nodes, at least, one node remains awake, limiting the sleep time to 50 %. Therefore, PSM is not suitable for low-energy, low-latency multi-hop WVSNs. Many solutions described in [50] allow increasing the energy efficiency by keeping a node in Deep Sleep mode when it is not involved in data transmissions. Next, we present solutions/schemes that use modified versions of PSM or PSM combined with other energy-efficient mechanisms.

If using IEEE 802.11 PSM all nodes in a multi-hop WVSN need to be time synchronised and periodically wake-up at the beginning of each beacon interval to exchange the Ad-hoc

Green Wireless Video Sensor Networks using a Low-Power Control Channel

M A P

tele

DOCTORAL PROGRAMME IN TELECOMMUNICATIONS

Green Wireless Video Sensor Networks using a

Low-Power Control Channel

Filipe Miguel Monteiro da Silva e Sousa

The Jury

M A P

tele

Abstract

Resumo

Acknowledgements

Contents

List of Figures

List of Tables

List of Acronyms

Chapter 1

Introduction

1.1

Motivation

1.2

Problem Statement

1.3

Objectives

1.4

Original Contributions

LPR range

1.5

Publications

1.6

Document Structure

Chapter 2

State-of-the-Art in Energy-Efficient

Solutions

2.1

Out-of-Band Control Oriented Solutions

2.2

MAC Oriented Solutions