An Energy-conscious Transport Protocol for Multi-hop Wireless Networks

We present a transport protocol whose goal is to reduce power consumption without compromising delivery requirements of applications. To meet its goal of energy efficiency, our transport protocol (1) contains mechanisms to balance end-to-end vs. local retransmissions; (2) minimizes acknowledgment traffic using receiver regulated rate-based flow control combined with selected acknowledgements and in-network caching of packets; and (3) aggressively seeks to avoid any congestion-based packet loss. Within a recently developed ultra low-power multi-hop wireless network system, extensive simulations and experimental results demonstrate that our transport protocol meets its goal of preserving the energy efficiency of the underlying network.

Wireless and Physical Security via Embedded Sensor Networks

Wireless Intrusion Detection Systems (WIDS) monitor 802.11 wireless frames (Layer-2) in an attempt to detect misuse. What distinguishes a WIDS from a traditional Network IDS is the ability to utilize the broadcast nature of the medium to reconstruct the physical location of the offending party, as opposed to its possibly spoofed (MAC addresses) identity in cyber space. Traditional Wireless Network Security Systems are still heavily anchored in the digital plane of "cyber space" and hence cannot be used reliably or effectively to derive the physical identity of an intruder in order to prevent further malicious wireless broadcasts, for example by escorting an intruder off the premises based on physical evidence. In this paper, we argue that Embedded Sensor Networks could be used effectively to bridge the gap between digital and physical security planes, and thus could be leveraged to provide reciprocal benefit to surveillance and security tasks on both planes. Toward that end, we present our recent experience integrating wireless networking security services into the SNBENCH (Sensor Network workBench). The SNBENCH provides an extensible framework that enables the rapid development and automated deployment of Sensor Network applications on a shared, embedded sensing and actuation infrastructure. The SNBENCH's extensible architecture allows an engineer to quickly integrate new sensing and response capabilities into the SNBENCH framework, while high-level languages and compilers allow novice SN programmers to compose SN service logic, unaware of the lower-level implementation details of tools on which their services rely. In this paper we convey the simplicity of the service composition through concrete examples that illustrate the power and potential of Wireless Security Services that span both the physical and digital plane.

The Sensor Network Workbench: Towards Functional Specification, Verification and Deployment of Constrained Distributed Systems

As the commoditization of sensing, actuation and communication hardware increases, so does the potential for dynamically tasked sense and respond networked systems (i.e., Sensor Networks or SNs) to replace existing disjoint and inflexible special-purpose deployments (closed-circuit security video, anti-theft sensors, etc.). While various solutions have emerged to many individual SN-centric challenges (e.g., power management, communication protocols, role assignment), perhaps the largest remaining obstacle to widespread SN deployment is that those who wish to deploy, utilize, and maintain a programmable Sensor Network lack the programming and systems expertise to do so. The contributions of this thesis centers on the design, development and deployment of the SN Workbench (snBench). snBench embodies an accessible, modular programming platform coupled with a flexible and extensible run-time system that, together, support the entire life-cycle of distributed sensory services. As it is impossible to find a one-size-fits-all programming interface, this work advocates the use of tiered layers of abstraction that enable a variety of high-level, domain specific languages to be compiled to a common (thin-waist) tasking language; this common tasking language is statically verified and can be subsequently re-translated, if needed, for execution on a wide variety of hardware platforms. snBench provides: (1) a common sensory tasking language (Instruction Set Architecture) powerful enough to express complex SN services, yet simple enough to be executed by highly constrained resources with soft, real-time constraints, (2) a prototype high-level language (and corresponding compiler) to illustrate the utility of the common tasking language and the tiered programming approach in this domain, (3) an execution environment and a run-time support infrastructure that abstract a collection of heterogeneous resources into a single virtual Sensor Network, tasked via this common tasking language, and (4) novel formal methods (i.e., static analysis techniques) that verify safety properties and infer implicit resource constraints to facilitate resource allocation for new services. This thesis presents these components in detail, as well as two specific case-studies: the use of snBench to integrate physical and wireless network security, and the use of snBench as the foundation for semester-long student projects in a graduate-level Software Engineering course.

How Well Can TCP Infer Network State?

The Transmission Control Protocol (TCP) has been the protocol of choice for many Internet applications requiring reliable connections. The design of TCP has been challenged by the extension of connections over wireless links. We ask a fundamental question: What is the basic predictive power of TCP of network state, including wireless error conditions? The goal is to improve or readily exploit this predictive power to enable TCP (or variants) to perform well in generalized network settings. To that end, we use Maximum Likelihood Ratio tests to evaluate TCP as a detector/estimator. We quantify how well network state can be estimated, given network response such as distributions of packet delays or TCP throughput that are conditioned on the type of packet loss. Using our model-based approach and extensive simulations, we demonstrate that congestion-induced losses and losses due to wireless transmission errors produce sufficiently different statistics upon which an efficient detector can be built; distributions of network loads can provide effective means for estimating packet loss type; and packet delay is a better signal of network state than short-term throughput. We demonstrate how estimation accuracy is influenced by different proportions of congestion versus wireless losses and penalties on incorrect estimation.

A Bayesian Approach for TCP to Distinguish Congestion from Wireless Losses

(This Technical Report revises TR-BUCS-2003-011) The Transmission Control Protocol (TCP) has been the protocol of choice for many Internet applications requiring reliable connections. The design of TCP has been challenged by the extension of connections over wireless links. In this paper, we investigate a Bayesian approach to infer at the source host the reason of a packet loss, whether congestion or wireless transmission error. Our approach is "mostly" end-to-end since it requires only one long-term average quantity (namely, long-term average packet loss probability over the wireless segment) that may be best obtained with help from the network (e.g. wireless access agent).Specifically, we use Maximum Likelihood Ratio tests to evaluate TCP as a classifier of the type of packet loss. We study the effectiveness of short-term classification of packet errors (congestion vs. wireless), given stationary prior error probabilities and distributions of packet delays conditioned on the type of packet loss (measured over a larger time scale). Using our Bayesian-based approach and extensive simulations, we demonstrate that congestion-induced losses and losses due to wireless transmission errors produce sufficiently different statistics upon which an efficient online error classifier can be built. We introduce a simple queueing model to underline the conditional delay distributions arising from different kinds of packet losses over a heterogeneous wired/wireless path. We show how Hidden Markov Models (HMMs) can be used by a TCP connection to infer efficiently conditional delay distributions. We demonstrate how estimation accuracy is influenced by different proportions of congestion versus wireless losses and penalties on incorrect classification.

SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks

We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources-this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.

DIP: Density Inference Protocol for wireless sensor networks and its application to density-unbiased statistics

Wireless sensor networks have recently emerged as enablers of important applications such as environmental, chemical and nuclear sensing systems. Such applications have sophisticated spatial-temporal semantics that set them aside from traditional wireless networks. For example, the computation of temperature averaged over the sensor field must take into account local densities. This is crucial since otherwise the estimated average temperature can be biased by over-sampling areas where a lot more sensors exist. Thus, we envision that a fundamental service that a wireless sensor network should provide is that of estimating local densities. In this paper, we propose a lightweight probabilistic density inference protocol, we call DIP, which allows each sensor node to implicitly estimate its neighborhood size without the explicit exchange of node identifiers as in existing density discovery schemes. The theoretical basis of DIP is a probabilistic analysis which gives the relationship between the number of sensor nodes contending in the neighborhood of a node and the level of contention measured by that node. Extensive simulations confirm the premise of DIP: it can provide statistically reliable and accurate estimates of local density at a very low energy cost and constant running time. We demonstrate how applications could be built on top of our DIP-based service by computing density-unbiased statistics from estimated local densities.

On the Interaction between Data Aggregation and Topology Control in Wireless Sensor Networks

Wireless sensor networks are characterized by limited energy resources. To conserve energy, application-specific aggregation (fusion) of data reports from multiple sensors can be beneficial in reducing the amount of data flowing over the network. Furthermore, controlling the topology by scheduling the activity of nodes between active and sleep modes has often been used to uniformly distribute the energy consumption among all nodes by de-synchronizing their activities. We present an integrated analytical model to study the joint performance of in-network aggregation and topology control. We define performance metrics that capture the tradeoffs among delay, energy, and fidelity of the aggregation. Our results indicate that to achieve high fidelity levels under medium to high event reporting load, shorter and fatter aggregation/routing trees (toward the sink) offer the best delay-energy tradeoff as long as topology control is well coordinated with routing.

End-to-End Inference of Loss Nature in a Hybrid Wired/Wireless Environment

End-to-End differentiation between wireless and congestion loss can equip TCP control so it operates effectively in a hybrid wired/wireless environment. Our approach integrates two techniques: packet loss pairs (PLP) and Hidden Markov Modeling (HMM). A packet loss pair is formed by two back-to-back packets, where one packet is lost while the second packet is successfully received. The purpose is for the second packet to carry the state of the network path, namely the round trip time (RTT), at the time the other packet is lost. Under realistic conditions, PLP provides strong differentiation between congestion and wireless type of loss based on distinguishable RTT distributions. An HMM is then trained so observed RTTs can be mapped to model states that represent either congestion loss or wireless loss. Extensive simulations confirm the accuracy of our HMM-based technique in classifying the cause of a packet loss. We also show the superiority of our technique over the Vegas predictor, which was recently found to perform best and which exemplifies other existing loss labeling techniques.

Utility-Based Decision-Making in Wireless Sensor Networks

We consider challenges associated with application domains in which a large number of distributed, networked sensors must perform a sensing task repeatedly over time. For the tasks we consider, there are three significant challenges to address. First, nodes have resource constraints imposed by their finite power supply, which motivates computations that are energy-conserving. Second, for the applications we describe, the utility derived from a sensing task may vary depending on the placement and size of the set of nodes who participate, which often involves complex objective functions for nodes to target. Finally, nodes must attempt to realize these global objectives with only local information. We present a model for such applications, in which we define appropriate global objectives based on utility functions and specify a cost model for energy consumption. Then, for an important class of utility functions, we present distributed algorithms which attempt to maximize the utility derived from the sensor network over its lifetime. The algorithms and experimental results we present enable nodes to adaptively change their roles over time and use dynamic reconfiguration of routes to load balance energy consumption in the network.

On the Interaction between TCP and the Wireless Channel in CDMA2000 Networks

In this work, we conducted extensive active measurements on a large nationwide CDMA2000 1xRTT network in order to characterize the impact of both the Radio Link Protocol and more importantly, the wireless scheduler, on TCP. Our measurements include standard TCP/UDP logs, as well as detailed RF layer statistics that allow observability into RF dynamics. With the help of a robust correlation measure, normalized mutual information, we were able to quantify the impact of these two RF factors on TCP performance metrics such as the round trip time, packet loss rate, instantaneous throughput etc. We show that the variable channel rate has the larger impact on TCP behavior when compared to the Radio Link Protocol. Furthermore, we expose and rank the factors that influence the assigned channel rate itself and in particular, demonstrate the sensitivity of the wireless scheduler to the data sending rate. Thus, TCP is adapting its rate to match the available network capacity, while the rate allocated by the wireless scheduler is influenced by the sender's behavior. Such a system is best described as a closed loop system with two feedback controllers, the TCP controller and the wireless scheduler, each one affecting the other's decisions. In this work, we take the first steps in characterizing such a system in a realistic environment.

JTP: An Energy-conscious Transport Protocol for Wireless Ad Hoc Networks

Within a recently developed low-power ad hoc network system, we present a transport protocol (JTP) whose goal is to reduce power consumption without trading off delivery requirements of applications. JTP has the following features: it is lightweight whereby end-nodes control in-network actions by encoding delivery requirements in packet headers; JTP enables applications to specify a range of reliability requirements, thus allocating the right energy budget to packets; JTP minimizes feedback control traffic from the destination by varying its frequency based on delivery requirements and stability of the network; JTP minimizes energy consumption by implementing in-network caching and increasing the chances that data retransmission requests from destinations "hit" these caches, thus avoiding costly source retransmissions; and JTP fairly allocates bandwidth among flows by backing off the sending rate of a source to account for in-network retransmissions on its behalf. Analysis and extensive simulations demonstrate the energy gains of JTP over one-size-fits-all transport protocols.

Fusion Artmap: A Neural Network Architecture for Multi-Channel Data Fusion and Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ARTMAP during supervised learning. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, become inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called paraellel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of them. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network. Fusion ARTMAP's multi-channel coding is illustrated by simulations of the Quadruped Mammal database.

A Distributed Outstar Network for Spatial Pattern Learning

The distributed outstar, a generalization of the outstar neural network for spatial pattern learning, is introduced. In the outstar, signals from a source node cause weights to learn and recall arbitrary patterns across a target field of nodes. The distributed outstar replaces the outstar source node with a source field of arbitrarily many nodes, whose activity pattern may be arbitrarily distributed or compressed. Learning proceeds according to a principle of atrophy due to disuse, whereby a path weight decreases in joint proportion to the transmitted path signal and the degree of disuse of the target node. During learning, the total signal to a target node converges toward that node's activity level. Weight changes at a node are apportioned according to the distributed pattern of converging signals. Three synaptic transmission functions, by a product rule, a capacity rule, and a threshold rule, are examined for this system. The three rules are computationally equivalent when source field activity is maximally compressed, or winner-take-all. When source field activity is distributed, catastrophic forgetting may occur. Only the threshold rule solves this problem. Analysis of spatial pattern learning by distributed codes thereby leads to the conjecture that the unit of long-term memory in such a system is an adaptive threshold, rather than the multiplicative path weight widely used in neural models.

Neural Pattern Recognition With Multi-Scale Pyramidal Coding and Selective Attention

A new neural network architecture for spatial patttern recognition using multi-scale pyramida1 coding is here described. The network has an ARTMAP structure with a new class of ART-module, called Hybrid ART-module, as its front-end processor. Hybrid ART-module, which has processing modules corresponding to each scale channel of multi-scale pyramid, employs channels of finer scales only if it is necesssary to discriminate a pattern from others. This process is effected by serial match tracking. Also the parallel match tracking is used to select the spatial location having most salient feature and limit its attention to that part.