A New Approach to Channel Access Scheduling in Ad Hoc Networks Lichun Bao School of ICS University of California, Irvine J.J. Garcia-Luna-Aceves School of Engineering University of California, Santa Cruz

University of California2 Existing Solutions for Channel Access: Random Access Scheme: –ALOHA, CSMA/CA (FAMA, MACA, MACAW, IEEE ) : with/without RTS/CTS handshakes. –Difficulties providing fairness, QoS. Scheduled Access Scheme: –Node/Link Activation. –FDMA/TDMA/CDMA in multihop networks: graph coloring problem — UxDMA. Administrator: UxDMA needs the whole network topology, while distributed solution needs local topology and schedule resolution. Administrator: UxDMA needs the whole network topology, while distributed solution needs local topology and schedule resolution.

University of California3 Our Solution: Scheduled Access Problem description: –Given a set of contenders Mi of an entity i in contention context t, how does i determine whether itself is the winner during t ? Topology dependence: –Exactly two-hop neighbor information required to resolve contentions. –Two-hop neighbors are acquired by each node broadcasting its one-hop neighbor set.

University of California4 Example Settings: Omni-directional Antenna; Time slotted channel access; Equal transmission range; 4 nodes; Each node knows its one- and two-hop neighbors — Mi.

University of California5 Goals to Achieve: Collision-free — avoid hidden terminal problem, no waste on transmissions; Fair — the probability of accessing the channel is proportional to contention; Live — capable of yielding at least one transmission each time slot.

University of California6 Illustrations by Example: time t 0123 j i l k i winsj & l wink wins

University of California7 Neighbor-aware Contention Resolution (NCR): In each contention context (time slot t): –Compute priorities –i is the winner for channel access if:

University of California8 Attributes of NCR: Collision freedom; Fairness; Liveliness; 2-coloring: –An entity colors itself if it red has the highest priority among its contenders. –Otherwise, it has transparent color.

University of California9 NCR-MI (Multiple Identities): Dynamic Resource Allocation. A node i may have I i pseudo identities. –k-th identity is denoted as –I i is dynamically chosen by i according to traffic requirement. –Each identity of i gives i a chance to win a contention. The more identities, the better chance of channel access.

University of California10 NCR-MI Specification: Compute the priority on each pseudo identity of every contender: –For l-th identity of contender k, we have: i is the winner for channel access one of its priority is the greatest among its contenders.

University of California11 Channel Access Probability: Dependent on the number of pseudo identities and the density of the neighborhood. Channel access probability: –Bandwidth allocation

University of California12 Channel access probability for individual nodes: Spatial channel reuse ratio: Bandwidth Allocation Example: j i l k

University of California13 Delay & Throughput Analysis: Data packet service at entity i modeled as M/G/1 queuing system with server vacation. Delay (Pollaczek-Kinchin formula): Throughput:

University of California14 Delay Curves:

University of California15 Channel Access Scheduling Protocols: Node Activation Multiple Access (NAMA): –Entity type: node –Time division: Block Section Part Time-slot

University of California16 NAMA Time Division Illustrated: 01 Section Membership Section: Neighbor Maintenance Block 012 Part Time Slot

University of California17 NAMA Illustrated: Fully connected network with 10 nodes. ID: 1~10. 1,5,6,8,102,3,4,7,9 Part 0Part 1 Contenders resolve contention using NCR 81,5, ,4,7 Section 1 1,105,864,92,3,7 Section 0 2,3,4,7,9 No occupied by anyone Everyone tries to use

University of California18 Neighbor Protocol: One-hop neighbor information broadcasting. –New node starting up. –Link addition and deletion. –Old neighbor going down can be treated as multiple link deletions. Membership section: send signals.

University of California19 Channel Access Scheduling Protocols (continued): Link Activation Multiple Access (LAMA): –Direct Sequence Spread Spectrum, available pseudo- noise code set: C pn –Received-Oriented Code Assignment (ROCA) –Contenders of node i : –Once Mi is decided, LAMA follows NCR.

University of California20 LAMA Illustrated: i j k a b c g f e d Node i tries to activate its adjacent links on code c Both j and k are assigned code c c c At time t, the priority of each node is computed i can activate either link (i,j) or (i,k).

University of California21 Channel Access Scheduling Protocols (continued): Pair-wise Link Activation Multiple Access (PAMA): –Contending entities are directed edges; –Priorities are computed for each link; –Dynamic code assignment: –Contenders of a link are its adjacent links.

University of California22 PAMA Illustrated: i a b c k g f Directional links 2. Only one direction shown for simplicity 3. Hidden terminal avoidance: link (i,k) and (f,g) assigned the same code — compare node priorities of i and f. c c

University of California23 Summary — Unified Algorithm: Determine the entity type (node/link); Find out the contender set; Run NCR to determine if the entity is active in the current time slot; Resolve hidden terminal problem.

University of California24 Performance (Delay — Fully Connected):

University of California25 Performance (Delay — Multi-hop Network):

University of California26 Performance (Throughput — Fully Connected)

University of California27 Performance (Throughput — Multi-hop)

University of California28 Comparison with Static Scheduling Algorithm (UxDMA):

University of California29 Coloring Efficiency Comparison with UxDMA:

University of California30 Problems with NAMA Inefficient activation in certain scenarios. –For example, only one node, a, can be activated according NAMA, although several other opportunities exist. —— We want to activate g and d as well. a f g c d e h b

University of California31 Node + Link (Hybrid) Activation Additional assumption –Radio tranceiver is capable of code division channelization (DSSS —— direct sequence spread spectrum) –Code set is C. Code assignment for each node is per time slot: i.code = i.prio mod |C |

University of California32 Hybrid Activation Multiple Access (HAMA) Node state classification per time slot according to their priorities. –Receiver (Rx): intermediate prio among one- hop neighbors. –Drain (DRx): lowest prio amongst one-hop. –BTx: highest prio among two-hop. –UTx: highest prio among one-hop. –DTx: highest prio among the one-hop of a drain.

University of California33 HAMA (cont.) Transmission schedules: –BTx — > all one-hop neighbors. –UTx — > selected one-hops, which are in Rx state, and the UTx has the highest prio among the one-hop neighbors of the receiver. –DTx — > Drains (DRx), and the DTx has the highest prio among the one-hops of the DRx.

University of California34 HAMA Operations Suppose no conflict in code assignment. Nodal states are denoted beside each node: –Node D converted from Rx to DTx. –Benefit: one-activation in NAMA to four possible activations in HAMA. a f g c d e h b 10-BTx 1-DRx 6-Rx 4-DRx 7-UTx 3-DRx 8-Rx 5-DTx

University of California35 Neighbor Protocol (Need) Purpose: propagate neighbor updates. Cannot be based on NCR — requires a priori neighbor information. Only way: –Random access. –Broadcast. –No acknowledgement: why? Efficiency, broadcast. –Use retransmission to improve reliability. Why not TSMA: Topology-dependent.

University of California36 Neighbor Protocol (Method) Insert random access section after ROMA. Send short signals carrying neighbor updates (256 bytes). Problem formulation: –How to regulate interval t and number n of retransmissions to have low latency to deliver messages with given (high) probability p.

University of California37 Neighbor Protocol (Results) –Reliability: deliver-probability p =99%. –Retransmission interval: t =1.44N — only depends on N (the number of two hop neighbors). –Number of retransmission: n =6.7≈7 — only depends on p. –Suppose 2Mbps bandwidth, 2 second delay, 20 two-hop neighbors — random access sections cost 9.6% of the channel resource.

University of California38 Performance Analysis Modeling –Infinite plane with node density ρ (100 nodes per 1000mX1000m area). –Transmission range r (0m~500m). Derive average per-node throughput according to node-distribution and node geometric relations. Analyze both NAMA and HAMA.

University of California39 Comparison between NAMA and HAMA HAMA has higher throughput than NAMA: –Similar at low transmission range r. –3-4 times higher throughput at higher r.

University of California40 Comparison with CSMA and CSMA/CA (1) Throughput of CSMA (CA) taken from the work of Yu et al. [ICNP’02]. Load conversion: –CSMA (CA) always fully loaded. Differ at channel access probability p’ and size l data. –HAMA load depends on packet arrival rate λ λ=p ’ · l data /(1+p ’ · l data ) Compare the throughput S in the one-hop neighborhood N= ρπr² (ρ: node density; r Tx range).

University of California41 Comparison with CSMA and CSMA/CA (2) Two scenarios: long data packet (100 time slots) and short data packet (10 time slot) Different contention levels in each scenario.

University of California42 Comparison with CSMA and CSMA/CA (3) HAMA gives the constant S at high load, whereas CSMA and CSMA/CA degrade. HAMA differs by the shift reaching the highest S. When the data packet is shorter, the collision vulnerable period becomes longer relatively in CSMA and CSMA/CA, thus lower throughput.

University of California43 Comparison with NAMA and UxDMA through Simulations UxDMA schedules broadcast only, like NAMA does. Network generated by placing 100 nodes in 1000mX1000m area. No movement. Transmission range: 100m, 200m, 300m, 400m. Code set size |C |=30. Simulation duration: 100,000 time slots.

University of California44 Throughput (1)

University of California45 Throughput (2) HAMA collected throughput of broadcast and unicast traffics separately. Overall throughput of HAMA and NAMA is compared with the theoretical analyses — matches well. NAMA is worse than UxDMA sometimes, HAMA is always better than UxDMA.

University of California46 Delay

University of California47 Delay Explained UxDMA always has lower delay. HAMA has separate delay attributes for unicast and broadcast, because they are transmitted using separate transmission opportunities. NAMA and HAMA have the same broadcast delay.

University of California48 Conclusions: Collision-free scheduling algorithm; Minimum topology information needed; Better throughput than static scheduling algorithms. More activation opportunities can be explored in NAMA —— HAMA. HAMA needs code division channelization. Theoretical analyses reveal higher throughput in HAMA than in NAMA. Scheduled approach gives higher throughput than random access approach (CSMA, CSMA/CA).