PHD DISSERTATION DEFENSE Receiver-Cost Cognizant Maximal Lifetime Routing in Embedded Networks: Model and Solutions Guofeng Deng Advised by Dr. Sandeep Gupta The IMPACT Lab Ira A. Fulton School of Engineering Arizona State University, Tempe

MPACT I Arizona State Ph.D. Dissertation Defense, Deng2 Outline Motivation –Intrinsic energy constrains in wireless ad hoc networks (WANET) –Existing research assumed that power for transmit dominates, and ignored power for receiving –For low power wireless devices, which are used widely nowadays, however, receiving power is not negligible. Background –WANET overview and the intelligent shipping container project –Receiving energy cost models Key Results –Receiver-cost cognizant maximal lifetime routing –Maximal lifetime routing in mobile networks Conclusion & Future Work

MPACT I Arizona State Ph.D. Dissertation Defense, Deng Highlights What it is all about: –investigating the impact of receiving energy costs by revisiting the maximal multicast lifetime routing problem, which is trivial if receiving packets is free Key contributions: –Receiving costs change the problem dramatically: NP-hard if receiving power is adaptive to received signal strength [Deng, Gupta, & Varsamopoulos, IEEE Comm. Letters 2008] NP-hard if nodes consume energy for overhearing as well [Deng & Gupta, ICDCN'06] –Handling receiving costs properly improve multicast lifetime compared with disregarding them: By 15% with no overhearing costs [Deng & Gupta, Globecom’06] By 60% with overhearing costs [Deng & Gupta, ICDCN'06] –First distributed algorithm to adapt to node mobility for maximal multicast lifetime [Deng, Mukherjee & Gupta, in preparation]

MPACT I Arizona State Ph.D. Dissertation Defense, Deng4 Wireless Ad Hoc Network Overview WANET –Distributed networks: nodes talk to others in the proximity directly –Wireless devices: low power, small form factor devices with short comm. range –Multicast medium: one transmission can reach multi-entities in proximity Features –Flexible & robust: no dependency on fixed infrastructure –Scalable : can accommodate large number of entities –Low cost, low maintenance, mobile, … Applications –Surveillance and rescue: environment monitoring, Body Area Network (BAN), … Challenges –Limited resources (e.g. energy, bandwidth)

MPACT I Arizona State Ph.D. Dissertation Defense, Deng WANET Application: Intelligent Shipping Container (ISC) Background: Global nature of today’s economy –90% of the world’s trade is transported in cargo containers –10 million cargo containers enter U.S. ports each year Motivations –Homeland security: only 5% can be inspected because of today’s limited time and money –Commercial values: lack of end-to-end visibility for supply chain and chain of custody Goal –Architecture design that meets various requirements –Verification of currently available technologies A joint effort between Intel Inc. & the Impact Lab.

MPACT I Arizona State Ph.D. Dissertation Defense, Deng ISC: Hierarchical Container Network Internal container network (within each container) –Wireless Sensor Network (WSN): collecting environmental parameters –Radio Frequency Identification (RFID): automatic and unique identification, multi-level tracking (e.g. products, packages, pallets, containers, etc) –Gateway: point of access from outside container, on scene data processing and storage External container network –WANET: interact with neighboring containers

MPACT I Arizona State Ph.D. Dissertation Defense, Deng ISC: Severe Energy Constraints MicaZ mote with MTS310 Sensor board –Broadcasts a packet every 10 sec with its voltage level –Uses the power saving mode (switching off radio and sensor board after readings) –2 new AA batteries –The base station (4 meters away) collects packets –The mote lasts about 46 days Had to use a car battery to power Stargate (gateway) and RFID reader for a 5-day shipment. Government regulation requires lifetime at least 1 year.

MPACT I Arizona State Ph.D. Dissertation Defense, Deng Motivation: Receiving Power is Not Negligible Most existing research aimed to conserve energy for transmitting packets and neglected energy consumed for receiving packets Receiving power is not negligible in low power devices Chipcon CC2420 (single-chip 2.4 GHz IEEE compliant RF transceiver) data sheet:

MPACT I Arizona State Ph.D. Dissertation Defense, Deng Motivation: Receiving Power Affects Route Optimality Example scenario: to transmit same packets from 1 to 2 and 3, i.e. multicast Goal: to transmit as many packets as possible before any node exhausts its battery Assumption: identical nodes, B is battery capacity, a link is associated with energy consumed for transmitting a packet over the link Energy cost for rcv a pkt Total number of packets rcved by 2 or 3 0 B/3 (node 1 dies first) B/4 (node 1 dies first) 4 B/6 (node 2 dies first) B/4 (all nodes die at same time)

MPACT I Arizona State Ph.D. Dissertation Defense, Deng Receiving Energy Cost Characterization Media Access Control protocol TDMA based: a node may switch off the transceiver based on some schedule, avoid overhearing irrelevant packets. But it will consume energy for receiving packets designated to it. Random access: a node may overhear transmissions in the proximity and consume energy for demodulating signals not interested in

MPACT I Arizona State Ph.D. Dissertation Defense, Deng Receiving Energy Cost Characterization Decoding techniques † Based on transmitter-receiver power tradeoff [Vasudevan et al. Infocom'06] Turbo decoder: energy consumed for decoding a signal is inversely proportional to signal strength †, i.e. adaptive Regular decoder: energy consumed for decoding a signal is independent on signal strength, i.e. constant

MPACT I Arizona State Ph.D. Dissertation Defense, Deng12 Receiving Energy Cost Models Adaptive Receiving Cost (Turbo decoder) Constant Receiving Cost (regular decoder) Overhearing Adaptive Receiving cost model (OAR)‏ Overhearing Constant Receiving cost model (OCR)‏ Overhearing Cost (Random access MAC protocols )‏ Designated Adaptive Receiving cost model (DAR)‏ Designated Constant Receiving cost model (DCR)‏ Designated Receiving Cost Only (TDMA based MAC protocols)‏ Objective: to investigate maximal multicast lifetime problems under each of these receiving energy cost models.

MPACT I Arizona State Ph.D. Dissertation Defense, Deng13 Maximal Multicast Lifetime Routing in WANET Multicast traffic –A single source generates multicast packets –A set of nodes in the network need to receive the packets Metrics –The duration until the first node in the network to fail due to exhausted battery State of the art –Solvable in polynomial time when the receiving energy cost is 0

MPACT I Arizona State Ph.D. Dissertation Defense, Deng Related work Energy efficient multicast routing –Reduce overall energy consumption for multicast traffic –Take advantage of multicast media Maximal lifetime multicast routing –Extend the duration until the occurrence of some application dependent critical events –Balance energy consumption among nodes –Static vs. dynamic approaches Overhearing energy costs –Studied for data-gathering routing Adaptive receiving costs –Studied for data-gathering routing

MPACT I Arizona State Ph.D. Dissertation Defense, Deng15 MaxMLT under DCR Problem ─ Maximizing multicast lifetime under the DCR model, i.e. designated constant receiving energy costs Designated Receiving Power algorithm (DRP) ─ In the directed network graph, there is a link (u,v) if u can be received by v when peak transmit power is used. ─ Convert the network graph to so called INverse longevity Graph (ING) – Run Prim’s algorithm on the ING to generate a multicast tree Result ─ Optimal solution of time complexity O(n 2 log n), where n is the number of nodes uv 1/ l(u,v)

MPACT I Arizona State Ph.D. Dissertation Defense, Deng MaxMLT-DCR: Simulation Results ZRP: Zero Receiving Power Network size: (density) number of nodes in the network All the nodes are destinations and have identical battery capacity and peak TX power. RX = peak TX for each node uFor each node u, select RX randomly between peak TX and 2X peak TX

MPACT I Arizona State Ph.D. Dissertation Defense, Deng17 MaxMLT under OCR Problem –Maximizing multicast lifetime under OCR, i.e. overhearing constant receiving energy cost Challenges –NP-hardness: reduce set cover to MaxMLT under OCR

MPACT I Arizona State Ph.D. Dissertation Defense, Deng MaxMLT under OCR: NP-hard Assumptions –identical battery capacity –all links are associated with same transmit power MaxMLT under OCR Set cover Source node Forwarding nodes Destination nodes Observations: –Node s will die first –Lifetime of resulting multicast tree is determined by the number of forwarders

MPACT I Arizona State Ph.D. Dissertation Defense, Deng19 MaxMLT-OCR: Heuristic Solution Link weight computation with various metrics: adding link (2,4) to the existing tree. 1 4 on-tree node non-on-tree node transmit costs taken into account link being considered ZRP DRP CRP PRP link that has been established PRP: Proximity Receiving Power algorithm CRP: Cumulative Receiving Power algorithm (extending DRP for comparison) receiving costs taken into account Note: Link metric defines how the receiving power is taken into account. overhearing costs taken into account

MPACT I Arizona State Ph.D. Dissertation Defense, Deng20 MaxMLT-OCR: Simulation Results‏ Identical battery capacity and peak TX power RX = peak TX RX = 2X peak TX

MPACT I Arizona State Ph.D. Dissertation Defense, Deng MaxMLT-OCR: A Mcast Tree Snapshot The source node is surrounded by a hexagram and the rest are destinations Solid lines constitute mcast trees A circle represents the transmit range of the node in the centre The diameter of a solid grey dot represents the magnitude of overhearing costs Observation: PRP tends to increase transmit power and reduce num of transmitters to decrease overhearing costs.

MPACT I Arizona State Ph.D. Dissertation Defense, Deng22 MaxMLT under DAR Problem –Maximizing the multicast lifetime under the DAR model, i.e. designated adaptive receiving costs. –Assuming discrete levels of transmitting and receiving power State of the art –A binary search optimal solution to a weaker problem, in which the multicast tree structure is given Challenges –NP-hardness

MPACT I Arizona State Ph.D. Dissertation Defense, Deng23 MaxMLT-DAR: Chain of Transforms A chain of reduction from X3C (exact cover by 3-sets) to MaxMLT under DAR Is there any m-arbor in k-subgraph? Decision problem of MAL: to seek a m-arbor whose lifetime is no less than some positive bound Maximal m-arbor lifetime: m-arbor is a tree defined in an auxiliary graph; any m-arbor can be mapped to a mcast tree in the original graph with same lifetime and vice versa Special case of MaxMLT: nodes can adjust transmit and receive power only in discrete levels. We also assume identical battery capacity.

MPACT I Arizona State Ph.D. Dissertation Defense, Deng24 MaxMLT-DAR: Chain of Transforms cont’d A WANET and its auxiliary graph. Each node has two transmit levels and two receive levels.

MPACT I Arizona State Ph.D. Dissertation Defense, Deng25 MaxMLT-DAR: Chain of Transforms cont’d Reducing X3C to AIK in a k-subgraph. The transmit (receive) vertices in each bipartite are sorted in ascending order using represented transmit/receive power levels. A flat through path: go through each bipartite once and only one

MPACT I Arizona State Ph.D. Dissertation Defense, Deng26 MaxMLT in Mobile Ad Hoc Networks Problem –Maximizing multicast lifetime when nodes are mobile Challenges –Dynamic networks: node mobility and residual energy changes –No distributed solutions exist Solution –MSL (Multicast Service Lifetime) Multicast lifetime definition suitable for mobile networks –DAMIL (Distributed and Adaptive Multicast Lifetime algorithm) Propose a new metric to decentralize routing decisions Distributed and adaptive solution that adopts the new metric

MPACT I Arizona State Ph.D. Dissertation Defense, Deng27 MaxMLT-MANET: MSL Quality of multicast service: –num of packets received (vs. time) –by some or all destinations (address fairness among destinations) –in some period of time (adapt to dynamic networks in timely manner) MSL is a measurement of quality multicast service received

MPACT I Arizona State Ph.D. Dissertation Defense, Deng28 MaxMLT-MANET: ‏Max-Lifetime Tree A new metric that leads to distributed algorithm –Link weight –Node weight [s,i] is a path from s to i (s-path) and W max is a large value Optimality : a multicast tree in which each node maximizes its weight is a maximum-lifetime multicast tree

MPACT I Arizona State Ph.D. Dissertation Defense, Deng29 MaxMLT-MANET: DAMIL Data structure: a status table contains an entry for each neighboring node and itself: – w i : node weight – p i : parent id – h i : hop-count (distance from the source) – f i : forwarding control boolean (FCB, whether to forward packets to some children) Periodic beacons (Control info is carried in periodic beacons) – (w i, p i, h i, f i ) Activities: – Each node repeatedly seeks the s-path that maximizes its weight – Upon receiving a beacon An entry is created if the sender is a new neighbour Build or refine s-paths for gain in node-weight Updates the FCB accordingly

MPACT I Arizona State Ph.D. Dissertation Defense, Deng30 MaxMLT-MANET: Example Node c moved to a new location. Assume symmetric link weight and Wmax = 99.

MPACT I Arizona State Ph.D. Dissertation Defense, Deng MaxMLT-MANET: Simulation Measurement –The quality of a multicast service is denoted by Q=(Δ,Γ,  ), where Δ,Γ, and  represent window size, destination threshold and data threshold respectively. –the MSL is defined as the period of time until the service quality drops below Q Comparison algorithms –WMST: updates a maximum lifetime tree periodically; outperforms most lifetime maximizing protocols in static networks –SS-SPST-E: a distributed energy minimizing multicast protocol designed to overcome the impact of node mobility.

MPACT I Arizona State Ph.D. Dissertation Defense, Deng MaxMLT-MANET: Simulation Results 50 nodes totally, D is a set of destinations, source generates four 512B packets per second.

MPACT I Arizona State Ph.D. Dissertation Defense, Deng MaxMLT-MANET: Simulation Results

MPACT I Arizona State Ph.D. Dissertation Defense, Deng34 Conclusion & Future Works Conclusion: –Showed receiving costs change the maximal multicast lifetime problem dramatically –Showed handling receiving costs properly improves multicast lifetime significantly compared with disregarding them –Proposed first distributed algorithm to maximize multicast lifetime in mobile ad hoc networks Future works: –Scavenging energy management

MPACT I Arizona State Ph.D. Dissertation Defense, Deng35 Publication G. Deng, S. K. S. Gupta, and G. Varsamopoulos, Maximizing Multicast Lifetime with Transmitter-Receiver Power Tradeoff is NP-Hard, IEEE Communications Letters, Vol. 12, No. 9, September 2008 G. Deng and S. K. S. Gupta, On Maximizing Network Lifetime of Broadcast in WANETs under an Overhearing Cost Model, ICDCN 2006, LNCS 4308 G. Deng and S. K. S. Gupta, Maximizing Broadcast Tree Lifetime in Wireless Ad Hoc Networks, IEEE GLOBECOM'06, San Francisco, CA Deng, G. and Gupta, S. K. S. (2005). Maximizing multicast lifetime in wireless ad hoc networks. In L. T. Yang & M. Guo (Eds.), High-Performance Computing: Paradigm and infrastructure (pp ). Hoboken, NJ: John Wiley & Sons. S. J. Kim, G. Deng, S. K. S. Gupta and M. Murphy-Hoye, Enhancing Cargo Container Security during Transportation: A Mesh Networking Based Approach, IEEE HST, Waltham, MA, USA, April S. J. Kim, G. Deng, S. K. S. Gupta and Mary Murphy-Hoye, Intelligent Networked Containers for Enhancing Global Supply Chain Security and Enabling New Commercial Value, COMSWARE, Bangalore, India, G. Deng, T. Mukherjee, and S. K. S. Gupta, DAMIL: A Distributed and Adaptive Algorithms to Extend Multicast Service Lifetime in MANETs, in preparation for IEEE Communications Letters.

MPACT I Arizona State Ph.D. Dissertation Defense, Deng36 Thank You!

MPACT I Arizona State Ph.D. Dissertation Defense, Deng37 Motivation: Survive Energy Constrains Replenish battery –Battery replacing: expensive and impractical for large scale networks, such as sensor networks –Explore ambient energy source: limited capability Consume energy intelligently –Energy efficiency: reduce total amount of energy consumed –Lifetime: enlarge network life span as a whole

MPACT I Arizona State Ph.D. Dissertation Defense, Deng38 Intelligent Shipping Container cont’d Internal Wireless Sensor Networks 2.4 GHz External Hosts Cellular Network RFID Reader MICAz mote Containers Stargate USB Memory Card MICAz mote 2.4 GHz Stargate Managing Internal network (hardware, power and security); data processing, & routing outgoing packets to external interface. GPS Receiver 1 51-pin PCMCIACompact Flash USB Ethernet RS232 GPRS PCMCIA Modem Compact Flash card MICAz mote Mobile Computing Computers at point of work (Handhelds) & at the Data Center. Held by custom officers and load/unload workers. Querying current and historical data and DB downloading from the logging systems. Enterprise Servers: Computers at the Data Center. Collecting real-time data from containers, managing DB & responding to critical events reported by containers. Sensors MICAz mote Sensors MICAz mote TelosB mote ML Cargo Tag MICAz mote Arch Rock Edge Server Linux computer running web services-based environment with web UI for setup, control, monitor, & management of diverse wireless sensor networks. Arch Rock DataLogger Low-power Embedded Linux computer running local data collection & management of diverse wireless sensor networks Ethernet INTER-Container TelosB mote Attached to nearby containers. Proximity motes form an ad hoc (multi-hop) inter-container network.

MPACT I Arizona State Ph.D. Dissertation Defense, Deng ISC: Hierarchical Container Network External Container Network A container forms and participates in networks with their neighbors dynamically. Internal Container Network The network inside a container is isolated from the dynamic changes outside a container.

MPACT I Arizona State Ph.D. Dissertation Defense, Deng40 ISC: Network Entities Stargate + MICAz mote + WiFi card + memory card: data collection and processing, database management. PDA: monitor and manually control Stargate, e.g. start/stop RFID reader TelosB motes with onboard sensors: environmental sensor Skyetek M8 RFID reader + Cushcraft antenna + MICAz mote: Read RFID tags and forward the reading via wireless interface Base station: startup control and monitoring MICAz motes + MTS310: environmental sensor

MPACT I Arizona State Ph.D. Dissertation Defense, Deng ISC: System Tests Tested in a standalone container over several months in Chandler, Arizona, US Tested in a container yard in a 3×3 stacked container configuration in South Kearny, New Jersey, US Tested during a 5-day shipment from Singapore to Kaohsiung, Taiwan

MPACT I Arizona State Ph.D. Dissertation Defense, Deng42 Background: Generic Receiving Power Form : receiving energy of node v : transmit energy of node u : base receiving energy per bit of v : monotonic non-increasing adaptive receiving energy function ranging from 0 to 1 : transmission rate of i (bps) : min transmit power of node i to reach node v : distance between nodes u and v : fading exponent : integer parameters that can be either 0 or 1 For example, under OCR, if for any i and j, then

MPACT I Arizona State Ph.D. Dissertation Defense, Deng43 Background: Designated Receiving Cost Adaptive Receiving Cost Constant Receiving Cost Overhearing Adaptive Receiving cost model (OAR)‏ Overhearing Constant Receiving cost model (OCR)‏ Overhearing Cost (Random access MAC protocols )‏ Designated Adaptive Receiving cost model (DAR)‏ Designated Constant Receiving cost model (DCR)‏ Designated Receiving Cost Only (TDMA based MAC protocols)‏ transmitter designated receiver not related to the transmitter transmit power receiving power

MPACT I Arizona State Ph.D. Dissertation Defense, Deng44 MaxMLT-OCR: PRP: A Heuristic Solution Take into account the effects of overhearing explicitly on both transmitter and receiver The weight of link (i,j) – inverse link longevity -- incorporates the overhearing cost of i caused by v; it also takes into account the overhearing costs of v and u due to adding link (i,j). Run Prim’s algorithm to generate a tree that minimizes the maximum link weight u i j k v Transmission link Overhearing link

MPACT I Arizona State Ph.D. Dissertation Defense, Deng45 Background: Overhearing Cost Adaptive Receiving Cost Constant Receiving Cost Overhearing Adaptive Receiving cost model (OAR)‏ Overhearing Constant Receiving cost model (OCR)‏ Overhearing Cost (Random access MAC protocols )‏ Designated Adaptive Receiving cost model (DAR)‏ Designated Constant Receiving cost model (DCR)‏ Designated Receiving Cost Only (TDMA based MAC protocols)‏ transmitter designated receiver not related to the transmitter transmit power receiving power

MPACT I Arizona State Ph.D. Dissertation Defense, Deng46 Background: Constant Receiving Cost Adaptive Receiving Cost Constant Receiving Cost Overhearing Adaptive Receiving cost model (OAR)‏ Overhearing Constant Receiving cost model (OCR)‏ Overhearing Cost (Random access MAC protocols )‏ Designated Adaptive Receiving cost model (DAR)‏ Designated Constant Receiving cost model (DCR)‏ Designated Receiving Cost Only (TDMA based MAC protocols)‏ transmitter receiver transmit power receiving power

MPACT I Arizona State Ph.D. Dissertation Defense, Deng47 Background: Adaptive Receiving Cost Adaptive Receiving Cost Constant Receiving Cost Overhearing Adaptive Receiving cost model (OAR)‏ Overhearing Constant Receiving cost model (OCR)‏ Overhearing Cost (Random access MAC protocols )‏ Designated Adaptive Receiving cost model (DAR)‏ Designated Constant Receiving cost model (DCR)‏ Designated Receiving Cost Only (TDMA based MAC protocols)‏ transmitter receiver transmit power receiving power

MPACT I Arizona State Ph.D. Dissertation Defense, Deng48 Background: Adaptive Receiving Cost cont’d Adaptive Receiving Cost Constant Receiving Cost Overhearing Adaptive Receiving cost model (OAR)‏ Overhearing Constant Receiving cost model (OCR)‏ Overhearing Cost (Random access MAC protocols )‏ Designated Adaptive Receiving cost model (DAR)‏ Designated Constant Receiving cost model (DCR)‏ Designated Receiving Cost Only (TDMA based MAC protocols)‏ transmitter receiver transmit power receiving power Based on transmitter-receiver power tradeoff [Vasudevan et al. Infocom'06]

MPACT I Arizona State Ph.D. Dissertation Defense, Deng49 MaxMLT-OCR:‏ Feasible Metrics Proposed metric: Proximity Receiving Power (PRP): Take into account: transmit power + cumulated receiving power of the transmitter, receiving power of the receiver, transmit power + cumulated receiving power of all the affected neighbors Possible metrics: –Zero Receiving Power (ZRP): transmission power only, i.e., assume 0 reception cost –Designated Receiving Power (DRP): transmitter's transmit power, receiver's receiving power –Cumulative Reception Power (CRP): transmit power + cumulated receiving power of the transmitter, receiving power of the receiver

MPACT I Arizona State Ph.D. Dissertation Defense, Deng50 MaxMLT-DCR: Solution Analysis Optimal analysis Result ─ Optimal solution of time complexity O(n 2 log n), where n is the number of nodes

MPACT I Arizona State Ph.D. Dissertation Defense, Deng ISC: Hierarchical Network Structure Server –At shipper’s control center –Communication with gateways via the External Container Network External Container Network –To support the communication between gateways and interface between the server and a gateway Internal Container Network –To support the communication between devices within a container (e.g. a gateway, a RFID reader, and sensors)