Underlying link-layer protocols of well-established wireless networks that use the conventional “store-and-forward” design paradigm cannot provide highly sustainable reliability and stability in wireless communication, which introduce significant barriers and setbacks in scalability and deployments of wireless networks. In this paper, we propose a Code Embedded Distributed Adaptive and Reliable (CEDAR) link-layer framework that targets low latency and balancing en/decoding load among nodes. CEDAR is the first comprehensive theoretical framework for analyzing and designing distributed and adaptive error recovery for wireless networks. It employs a theoretically sound framework for embedding channel codes in each packet and performs the error correcting process in selected intermediate nodes in a packet's route. To identify the intermediate nodes for the decoding, we mathematically calculate the average packet delay and formalize the problem as a nonlinear integer programming problem. By minimizing the delays, we derive three propositions that: 1) can identify the intermediate nodes that minimize the propagation and transmission delay of a packet; and 2) and 3) can identify the intermediate nodes that simultaneously minimize the queuing delay and maximize the fairness of en/decoding load of all the nodes. Guided by the propositions, we then propose a scalable and distributed scheme in CEDAR to choose the intermediate en/decoding nodes in a route to achieve its objective. The results from real-world testbed “NESTbed” and simulation with MATLAB prove that CEDAR is superior to schemes using hop-by-hop decoding and destination decoding not only in packet delay and throughput but also in energy-consumption and load distribution balance.

We revisit the classic object tracking problem with a novel and effective, yet straightforward distributed solution for resource-lean devices. The difficulty of object tracking lies in the mismatch between the limited computational capacity of typical sensor nodes and the processing requirements of typical tracking algorithms. In this paper, we introduce an in-network system for tracking mobile objects using resource-lean sensors. The system is based on a distributed, dynamically-scoped tracking algorithm which alters the event detection region and reporting rate based on object speed. A leader node records the detected samples across the event region and estimates the object's location in situ. We study the performance of our tracking implementation on an 80-node test bed. The results show that it achieves high performance, even for very fast objects, and is readily implemented on resource-lean sensors. While the area is well-studied, the unique combination of algorithmic features represents a significant addition to the literature.

Network simulation is an important tool for testing and evaluating wireless sensor network applications. Parallel simulation strategies improve the scalability of these tools. However, achieving high performance depends on reducing the synchronization overhead among simulation processes. In this paper, we present an optimistic simulation algorithm with support for backtracking and re-execution. The algorithm reduces the number of synchronization cycles to the number of transmissions in the network under test. We implement SnapSim, an extension to the popular Avrora simulator, based on this algorithm. The experimental results show that our prototype system improves the performance of Avrora by 2 to 10 times for typical network-centric sensor network applications, and up to three orders of magnitude for applications that use the radio infrequently. We also implement a distributed version of SnapSim, D-SnapSim, which runs on a cluster. The experimental results show that D-SnapSim further improves the performance of SnapSim by up to 10 times for applications that use the radio frequently.

Underlying link-layer protocols of wireless networks use the conventional “store and forward” design paradigm cannot provide highly sustainable reliability and stability in wireless communication, which introduce significant barriers and setbacks in scalability and deployments of wireless networks. In this paper, we propose a Code Embedded Distributed Adaptive and Reliable (CEDAR) link-layer framework that targets low latency and high throughput. CEDAR is the first comprehensive theoretical framework for analyzing and designing distributed and adaptive error recovery for wireless networks. It employs a theoretically-sound framework for embedding channel codes in each packet and performs the error correcting process in selected intermediate nodes in packet's route. To identify the intermediate nodes for the en/decoding for minimizing average packet latency, we mathematically analyze the average packet delay, using Finite State Markovian Channel model and priority queuing model, and then formalize the problem as a non-linear integer programming problem. Also, we propose a scalable and distributed scheme to solve this problem. The results from real-world testbed “NESTbed” and simulation with Matlab prove that CEDAR is superior to the schemes using hop-by-hop decoding and destination-decoding not only in packet delay but also in throughput. In addition, the simulation results show that CEDAR can achieve the optimal performance in most cases.

Due to the limited computational and energy resources available on existing wireless sensor platforms, achieving high-precision classification of high-level events in-network is a challenge. In this article, we present in-network implementations of a Bayesian classifier and a condensed kd-tree classifier for identifying events of interest on resource-lean embedded sensors. The first approach uses preprocessed sensor readings to derive a multidimensional Bayesian classifier used to classify sensor data in real time. The second introduces an innovative condensed kd-tree to represent preprocessed sensor data and uses a fast nearest-neighbor search to determine the likelihood of class membership for incoming samples. Both classifiers consume limited resources and provide high-precision classification. To evaluate each approach, two case studies are considered, in the contexts of human movement and vehicle navigation, respectively. The classification accuracy is above 85% for both classifiers across the two case studies.