Introduction

1. Introduction • In recent years, the explosive deployment of Wireless LANs worldwide has brought the entire gamut of Web Applications to our fingertips. • Although existing systems provide a wide range of services, they are highly inefficient. • The main goal of our ongoing research is to optimally allocate resources among users in Wireless Networks. • Bandwidth and Energy are primary resources of interest. • Our framework studies a general multi-hop network. • Cellular and Access networks are special cases.

2. Applications of our Work • Data Services in Cellular Networks, similar to IEEE802.11, HDR etc. • Access Networks: Existing WLANs can greatly benefit by use of multiple hops to the base-station. • Sensor Networks: • Military Applications: Scatter smart devices over a field. Devices configure themselves to form a high speed network. An instant platform for communications in rough terrain. • Commercial Applications: Use sensor nodes to gather information about traffic conditions, volcanoes, earthquakes etc.

3. Problem Scenario

4. Decentralized Control

5. Notations and Definition • N user network. • Routes between users are given. • A directed link e = {i,j} is a valid signal transmission from node i to node j. Total of L links in the network. • A subset of L links is called a transmission mode. • There are 2^N transmission modes for a N user system. • Link Scheduling Policy: The time fractions associated with each transmission mode constitutes a scheduling policy. • Power Control Policy: The power used by each node in each slot constitutes a power control policy.

6. User Objective Vs Network Objective • User Objective: Each user would like to get the highest possible rate while using a limited amount of energy. • Network Objective: The network may choose to maximize the total data transferred for a given amount of energy used. • Alternately, a network may choose to minimize the total amount of power consumed while meeting minimum rate guarantees per user. • We assume that the connections supported are long enough and users always have data to transmit. E.g. file transfers, video/audio streaming etc. For short-life connections we could employ lightweight heuristics instead.

7. Current Approaches • Most approaches decouple link scheduling from power control policy. We propose an integrated approach. • Links are scheduled such that the interference experienced by the scheduled link is negligible (~ ambient noise). • Theme: Schedule nodes that are far away from each other simultaneously. • Such approaches allow users to transmit at peak power. • These approaches exploit spatial diversity by allowing multiple transmissions concurrently. • Sub-optimal with respect to system capacity maximization. They do not exploit spatial diversity optimally. • Overhead: Require limited processing of topology information but compute the link schedule using a centralized entity.

8. Conflict-Free Link Scheduling Policies • Graph Theoretic Approach. • If a node is transmitting is a slot, nodes in its vicinity cannot transmit in that slot. • The underlying assumption in most link scheduling algorithms is to limit interference to a very small value (~ ambient noise) • For a N node multi-hop network: The problem of computing the transmission schedules with optimal throughput is NP-complete Computing a Maximum Independent Set in a Graph. • Polynomial time approximation algorithms have been proposed.

9. Previous Work on Link Scheduling • Work Done by: Victor Li & Ji-Her Ju • Consider a single channel TDMA network with N mobiles. • The TDMA frame consists of q sub-frames each q slots long. • Each user is allocated q slots, one in each sub-frame. • Problem: Maximize the worst-case throughput of the network such that all users have at least 1 slot in each sub-frame to transmit. • Throughput : Defined as number of conflict-free transmissions in a frame. • Collision occurs when 2 or more nodes within a 1-hop distance in connectivity graph transmit simultaneously. • A central controller allocates q slots per user so that there are at most k collisions between any two users in a frame.

10. Work on Link Scheduling Cont… • Inputs to the Algorithm are: • N : total number of nodes • D : Maximum Degree of any node in the network • Given N and D, the algorithm finds a suitable q and k so that the worst-case Tput, (q – k D)/(q^2) is maximized. • A centralized entity computes the schedule and broadcasts it to all the nodes in the network. • Pros Cons Maximizes the Worst-case Scheduling policy is Centralized. Throughput. Blind to Changing Channel Simple to compute Schedules Conditions. Schedules can be updated Fails to exploit the peak wireless infrequentlychannel, unlike our approach, HDR • Topology Immune Algorithm: N and D are slowly varying quantities, but assumes worst case scenario.

11. Joint Link and Packet Scheduling • Work done by: Timely Research Group, UIUC. • Proposed a suite of Wireless Fair Queuing (WLFQ) algorithms to achieve packet level fairness in Cellular Networks. • WLFQ algorithms assumed that users could transmit in every slot, not the case in the multi-hop scenario. • Extend the notion of Wireless Fair Queuing to the multi-hop network paradigm. • They incorporate the effects of spatial diversity into fairness. • Link Schedules are determined based on a simple heuristic. • The WLFQ algorithm is augmented to account for link schedules.

12. Joint Link and Packet Scheduling Cont… • A Centralized entity takes as input the flow weights at each node, and the set of neighbors to compute link schedules. • How do nodes communicate with the centralized entity? • A commonly proposed technique to communicate update information to a central entity is to maintain a minimum spanning tree among the N nodes • Nodes in the vicinity (2-hop) of each other have an edge in the flow contention graph G(V,E). • Packets whose deadlines are about to expire are scheduled first. The rest of the links are chosen based on MIS. • If flow i* has been picked based on WLFQ, the remaining flows are picked such that they form a Maximum Independent Set (MIS) in the sub-graph G – N(i*), where N(i*) is the set of nodes in the vicinity of flow i*. • Computing an MIS is an NP-complete problem.

13. Maximum Independent Set • They resort to a polynomial time approximation algorithm to compute the MIS of the graph. • Given a graph G(V,E), a independent setS of a graph is a set of nodes in V such that if nodes {i,j} is an edge in the graph, then either i or j belong to S. • MIS: Independent set with the highest cardinality. • Choose initial set of links L_i based on packet deadlines assigned by WLFQ at different nodes. • For each node i, pick node with the minimum degree in the sub-graph G – N(i) for concurrent transmission. • The final set is the set of concurrent scheduled transmissions. • Approximation ratio :(D + 2)/3 • Runtime: O(N^2)

14. System Model • N user network. • Slotted CDMA multiple access network. • Routes between users are given. • A link e = {i,j} is a valid signal transmission from node i to node j. Total of L links in the network. • A subset of L links is called a transmission mode. • Nodes can transmit and receive in the same slot. • A node can transmit to multiple receivers at the same time. • Nodes can receive data from multiple transmitters concurrently. • Total amount of bandwidth available: W

15. System Model Contd… • Path-loss from node i to node j is G(i,j). • Assume a slow fading channel. i.e. G(i,j) changes slowly with time (frame length). • The power of signal transmission of node i to node j : P(i,j) • The signal to interference-plus-noise ratio for node i’s transmission at node j is: SIR(i,j) • The link capacity characterization: C(i,j) • Shannon Capacity: C(i,j) = log(1 + SIR(i,j) ) is not delay limited, assumes interference has a gaussian distribution. • We consider linear characterization: C(i,j) = W’ SIR(i,j) • Our characterization of capacity: Loss-rate bounded and Delay limited and thus practical. This characterization is widely used in the research world.

16. System Model Contd. • Rate of link e = {i, j} : X(i,j) = C(i,j) • The rate for link e in slot k is Xk(i,j) = W’ SIRk(i,j) • The weight associated with link e is Ø(e) • Refer to the *.pdf file for the continuation of this slide.

17. Mobile Ad Hoc Networks Autonomous Distributed Systems All mobile nodes, all wireless connections

18. What we have done? • Problem A: Find a linkscheduling and power control policy that maximizes the average multi-hop network capacity subject to peak power constraint per node and average power constraint per node (link). • Problem B: Find alink scheduling and power control policy that minimizes the total power consumed in the multi-hop network subject to peak power constraints per node and minimum average rate guarantees per node (link). • We have developed algorithms to solve both the above problems

19. Observations • Some observations: • Problem A and Problem B have objective functions with at most 2^N variables and N+1 constraints. • Solving problems of such size is hard, we use convex duality approach to solve the above problem. • The complexity of our algorithm is a low order polynomial in M:where M is the number of allowed transmission modes. Algorithm converges in a finite number of steps. • Complexity of our algorithm can be further reduced if we assume that: Nodes cannot transmit and receive at the same time. • The number of optimal transmission modes is typically (<= N+1). • As the level of ambient noise increases, the number of concurrent transmissions increases as well.

20. Cellular Network Paradigm • Highly Practical for Cellular Networks: For an N-user K- base station system with a total of M transmission modes per base station, the worst-case complexity of our algorithm is O(MK). • The average case (more realistic) complexity is significantly less. • Our algorithm is exactly the same for both uplink and downlink communications. • Alternately, one could use a powerful decoding scheme, Successive Interference Cancellation (SIC) to get high rates. • SIC is only effective if interference has a Gaussian distribution. • This is however not practical for multi-hop networks.

21. Simulation Environment • Six user single cell network. • Users have an average power = Peak Power/4 • Compare simulations with respect to K-TDMA policy. • K-TDMA transmission policy • The total number of concurrent transmissions is equal to K. • Each transmission mode gets to transmit for equal amounts of time. • This policy meets the average power constraint with equality. • HDR is a 1-TDMA policy • Our policy outperforms all other polices for all values of ambient noise (external interference).

22. Simulation Results

23. Simulation Results Cont…

24. A Note on Energy Efficiency • Energy Efficiency Quotient = (total data transferred/ total power consumed): • Solutions of Problem A and Problem B: Near Optimal Energy Efficient. • Use the solution of Problem A as inputs to Problem B • Solution to Problem B is optimally energy efficient with respect to the network in the real sense of the word. • The final solution also maximizes system capacity, meets average minimum rate guarantees and average power constraints per node (link).

25. Conclusions • Solved two resource allocation problems in the realm of wireless networks. • Performs better than HDR for dense networks. • Unified Link Scheduling and Power Control Approach: first known results. • Developed a suite of low-complexity near optimal heuristics. • Developed an online routing policy, not mentioned here. • General Idea: Opportunisticin the short-term (slot-level), fair in the long term (frame or sub-frame-level). • Identified a number of applications for our results. • Would like to better understand the dynamics of the wireless channel using a real test-bed.

26. Current work on test-bed implementation • Monarch atCarnegie Mellonwere the first toimplement a multi-hop network test-bed. • They implemented dynamic source routing (DSR) for nodes in a multi-hop network. • Insignia at Columbia Univ. integrated an adaptive QoS module with Monarch’sDSR module. • Code for both implementations is freely available for BSD Unix. • We must use their work to our advantage.

27. What we propose to do? • Set up a programmable test bed with a few laptops with IEEE802.11 cards. • Set up a few access-points campus wide. • Implement our integrated routing, link/packet scheduling and power control policy. • Short-term objective: Study a network of stationary nodes. • Long-Term objective: Study a network of mobile nodes.

28. Monarch Project • Among the first university-based multi-hop network test bed implementations. • Setting: Outdoor Environments, Mobile nodes. • Constant topology changes causes need for dynamic routing policies. • 8 node multi-hop network • 2 stationary nodes, 5 mobile nodes and 1 roving node that monitors the others. • 8 Laptops equipped with • Lucent Wavelan-1 cards. • GPS receiver to track position: Accurate to 1 meter. • Laptops and accessories racked up in rented cars. • Implement Dynamic Source Routing Module on all laptops.

29. How does DSR Work? • Route Discovery and Route Maintenance. • A source node S queries its neighbors with a Route Request packet. • Neighbors with a route to destination D, send all path information from themselves to D, with a Route Reply packet. • Node S chooses one of many paths arbitrarily, but caches the all of them. • If no path can be found to node D, the query is flooded into the network. • If a route to D is then found, node S and all intermediate nodes cache this route. • The gateway also checks if D lies within its subnet or not.

30. How does DSR Work? Cont… • If the gateway finds a route to D, it replies back to node S. • Else, it sends a Route Error packet to S. • How do nodes know who their neighbors are? • Through IEEE802.11’sLink-layer ACK mechanism. • By reading GPS information embedded in data packets periodically. • If a path breaks due to node mobility, a Route Error message is sent to node S. • DSR: On-Demand Routing. • Packets used the IPv6 extension header processing framework. • Aggressive querying of neighbor’s cache limits Route Request propagation. • Can be greatly improved with power control.

31. In-Lab Network Emulation • A useful tool for network emulation was a MAC-filter. • MAC-filter: a filter that only processes legitimate packets. • Drops packets if their source IP addr. matches any on the forbidden src IP addr. list. • Nodes used a trace file of routing table entries and MAC-filter lists to perform their respective route discovery operations. • Enabled routing of physically close nodes through an intermediate node. • Allowed them to emulate a multi-hop network in a single room. • Enabled easier testing of a wide range of scenarios. • Easier debugging and validation of their code.

32. Logging and Support Utilities • Visualization Tools: Track nodes using GPS and view 1- second interval snapshots at the field office. • Helpful in spotting unpredictable behavior. • Sieve out packet losses due to channel errors from those due to routing errors in post-run analysis. • Nodes use tcpdump() to record per-packet information. • They record signal strength (SIR) and signal quality (Frame error rate) for each received packet. • Packet arrival times and sequence numbers are also recorded. • Post-processing: Compute the goodput of the system after each test-run. • Bottom Line: Knowledge of node positions is necessary to diagnose network dynamics in real-time and non-real-time.

33. Lessons Learned • Lack of route diversity with 8 nodes. • Hard to construct multiple routes between nodes. • Our solution to that: Use Power Control. • Multi-level priority queues are worth implementing. • Control Information should be given higher priority over data. • In-lab testing is critical for success. • Use of MAC-filter enabled them to test diverse scenarios. • Use of GPS receivers, provided valuable insight about the goings-on during test-runs. • Visualization tools aided in on-line and off-line diagnosis. • Wireless propagation: not what you would expect. • Personnel management was not trivial.

34. The Insignia Project Provides something better than best effort service for some flows, e.g., video, voice. Hard QoS guarantee not possible in MANET  Adaptive QoS  Service Differentiation e.g., QoS insensitive flows can be serviced in best effort manner: e-mail QoS sensitive flows should be treated in better than best effort manner

35. INSIGNIA Features • Approach • Adaptive QoS approach • Service Differentiation via packet prioritization To provide adaptive QOS  Fast Reservation  Fast Restoration  QoS reporting – a feedback mechanism  Adaptation according to network conditions

36. INSIGNIA Principles In band signaling  to be responsive - requires only single packet on new path to initiate the restoration after rerouting - explicit out-of-band signaling is not responsive enough and often fails to reach the target mobile nodes Soft-state  for management and maintenance of resource reservations  first packet on new path create states (if necessary) and subsequent packets refresh the previous associated reservations en-route •  outstanding reservations and states automatically time out • (i.e., typically in seconds range.)

37. INSIGNIA IP option SERVICE MODE PAYLOAD INDICATOR BANDWIDTH INDICATOR BANDWIDTH REQUEST RES/BE BQ/EQ BW_IND MAX MIN 1 bit 1 bit 16 bits 1 bit SERVICE MODE : adaptive (RES) service / best effort service PAYLOAD INDICATOR : base quality (BQ) packet / enhanced quality (EQ) packet BANDWIDTH INDICATOR : reflects the resource availability en route BANDWIDTH REQUEST : indicates the max/min BW requirements

38. Legend Packets Received at Destination Mobile Node RES BQ MAX Max_BW Min_BW RES/BQ packet RES/EQ packet RES EQ MAX Max_BW Min_BW BE packet MAX reserved link MIN reserved link Reservation Set-up M2 MS MD M1 M3 M4 QOS report : MAX reservation established

39. Re-routing / Restoration M2 immediate restoration Legend RES/BQ packet RES/EQ packet BE packet MAX reserved link MIN reserved link M2 M2 MS MD M1 M3 Rerouting Rerouting M4

40. EQ degradation : degraded to minimum service M3 M3 M3 M5 M5 M5 Legend Packets Received at Destination Mobile Node RES BQ MIN Max_BW Min_BW RES/BQ packet RES/EQ packet BE EQ - Max_BW Min_BW BE packet MAX reserved link MIN reserved link Re-routing / Degradation MS MD M1 Rerouting Rerouting M4 Rerouting Rerouting bottleneck node

41. Packets sent by Source Mobile Node in MIN service constant resource availability detected constant resource availability detected RES BQ MAX Max_BW Min_BW resource now available resource now available QOS report : Scale Up Legend Pkts Received at Destination in MIN service RES/BQ packet RES BQ MAX Max_BW Min_BW RES/EQ packet BE packet MAX reserved link MIN reserved link Adaptation : Scale Up MAX service re-initiated MS MD M1 M5 bottleneck node bottleneck node M4 QOS report : Scale Up

42. INSIGNIA Summary INSIGNIA performs fast resource reservation responsiverestorations and, timely adaptations. QoS reports function as - notification of flow set up completion, - report for on-going service quality and - also used for triggering adaptation process. In band nature allows INSIGNIA to be responsive enough to deal with the frequent rerouting Soft state approach guarantees the release of outstanding reservations on old path

43. How to Improve Network Performance • Monarch and Insignia are implemented on a IEEE802.11 WLAN. • The above projects test their network under light-to-moderate loads. • IEEE802.11 does not scale well with the number of users. • In dense neighborhoods, high contention for data slots results in frequent collisions in the reservation slots. • IEEE802.11 uses the point coordination function, servicing one node at a time under such circumstances. • We need the same functionality in a multi-hop network, i.e. link scheduling by the access point. • Our resource allocation policies achieve substantial improvements in network capacity, theoretically. • We would like to verify the profundity of our results in a real network.

44. Our Proposal • Implement Power Control. • Nodes can change their power levels depending on the population density around them. • Power control can be done using certain WLAN cards. e.g. 3Com cards. • Implement a variety of link and packet scheduling policies. This requires nodes in the network to be in sync with each other. • Nodes in an IEEE802.11 network are already synchronized to their Access Point (AP). • AP’s beacon the correct time to all the nodes in their domain. • AP’s could choose to flood the network with the correct time. This method might not be accurate enough. • Alternately, GPS receivers at nodes can synchronize them to desired accuracy (sub-millisecond). • Implement alternative routing strategies.