Information Sciences and Systems Lab Introduction Our research lies in the interface of communications, signal processing (computing) and networking. Sponsored by National Science Foundation, our recent work focuses on distributed and collaborative information processing and crosslayer design (with a physical layer emphasis) in wireless ad hoc and sensor networks, and associated information-theoretic and computation-theoretic analysis. Energy-Efficient Distributed Detection Virtual MIMO Communications in Wireless Networks Distributed Detection Via Multihop Fusion Significant energy reduction compared with direct transmission Multihop forwarding Histogram Fusion Multihop Log-likelihood Ratio (LLR) Fusion Joint optimization of fusion rules and transmission structure LLR fusion performs best. S Nodes in close proximity can cooperate in transmission to form a virtual MIMO system Virtual MIMO reduces the energy consumption for the same throughput and BER target Communications - Relay/Cooperative diversity - Virtual MIMO - Distributed source coding -- Information spreading … Determination of the optimal transmission strategy depends on many interacting factors Distributed Detection With A Multiple Access Channel MAC fusion achieves centralized performance asymptotically with a properly designed local mapping rule. Better bandwidth efficiency and detection performance than PAC (Parallel Access Channel) fusion Signal Processing - Detection and estimation - Localization and tracking - Statistical inference - Distributed computation … Networking - Belief propagation - Network coding - Clustering - Geographic routing … Error rate for detection of a sinusoid signal in correlated Gaussian noise Reach back problem Receiver equipped with multiple antennas Linear multiuser detector Transmission is successful as long as received SIR is greater than a threshold (multi-packet reception) Medium Access Schemes Round-robin Slotted ALOHA Maximum Throughput Scheduling Multi-antenna Receiver Sensor Nodes Accelerating Distributed Consensus Optimal Throughput and Energy Efficiency for Sensor Networks: A Crosslayer Study Dynamic Self-Calibration in Wireless Networks: A Belief Propagation Approach We investigate distributed algorithms leading to faster computation of aggregate functions of node values Cluster-based Solutions: A cluster acts as an entity and information exchange among clusters Faster convergence due to better connectivity Less communication burden Belief propagation is a general class of distributed message-passing algorithms, intended to solve the NP-hard probabilistic inference problems Its application in wireless networks requires a reverse thinking, intended to serve as a general framework for collaborative information processing and dissemination Particle filtering can be exploited for efficient message composition, processing and transmission in practice For given MAC schemes and detectors, we optimize average number of transmissions per slot and the transmission power for Throughput Maximization Throughput-constrained Energy Minimization For more information, contact: Dr. Huaiyu Dai 2108 Engr Bldg II Phone: 919-513-0299 Email: Huaiyu_Dai AT ncsu.edu Nonreversible Random Walk Based Solutions Known schemes are based on reversible chains We propose a Location-aided Distributed Averaging (LADA) algorithm based on nonreversible chains Node classifies neighbors by their relative locations Converges substantially faster than known schemes