This paper presents a computationally efficient semi-supervised learning (SSL) framework designed for real-time, on-device learning in resource-constrained IoT-based health monitoring systems. The proposed methods focus on minimizing redundant computation and memory usage, which are directly linked to energy consumption in embedded platforms. First, Mini-Batch K-Means clustering is employed as an alternative to full-batch clustering, reducing per-iteration computational time and memory footprint, particularly for low to moderate-dimensional datasets. To further enhance efficiency, two unsupervised convergence detection mechanisms, β-stop and ζ-stop are introduced to autonomously halt clustering once model stability is achieved, preventing unnecessary retraining and reducing energy overhead. β-stop monitors the stabilization of clustering iterations per learning cycle, while ζ-stop tracks the rate of cluster growth as a convergence indicator. Experimental evaluations on three representative IoT health monitoring datasets:Smart Hydration Tracking (SHT), Human Activity Detection (HAD), and Infant Activity Detection (IAD)demonstrate that the proposed strategies reduce computational time by up to 45% and CPU memory consumption by up to 30% without compromising classification accuracy. The results confirm the framework’s scalability, energy efficiency, and suitability for reliable, real-time semi-supervised learning on embedded IoT devices.

This paper presents a deep learning framework for energy-and bandwidth-aware time-series regression for IoT and wireless sensor networks. Leveraging cross-modality structural data dependencies, the approach uses a multivariate regression approach for predicting many different sensor modality time-series from a fewer number of sensor modality data. This reduces the communication bandwidth usage and energy consumption for resource-constrained sensor nodes. In addition, the approach can be leveraged for reducing number of sensors in a system, thus reducing the hardware and associated costs in IoT-based systems. This is done while ensuring successful reconstructions of all the original time-series for the necessary sensing modalities. One key attribute of the proposed framework is that the processing load is placed entirely at the receiver side, thus making it suitable for resource-constrained sensors and IoT based systems. The developed methodology is validated using data collected by a greenhouse sensor IoT system deployed over a period of three years. The results demonstrate the ability of the proposed approach for successful prediction of many sensor modality times series from only one time-series, representing the energy harvesting super-capacitor voltage of the sensor IoT system. The trade-offs between performance, bandwidth usage, number of sensing modalities, scalability, and computation cost are analyzed for providing design and implementation guidelines.

This paper introduces an Asymmetric Autoencoder (AAE)-driven data compression framework for efficient management of energy, bandwidth, and transmitter complexity in a Wireless Sensor Network (WSN). WSNs are often limited by their ability to process and transmit high-dimensional data due to various constraints, including available energy, processing cycles, and transmission capacity. Achieving an application-specific downstream task executed in a remote receiver under the influence of such sensor node constraints is the focus of the proposed methodology. It places a data compression encoder and decoder at the transmitter and the receiver, respectively. The architecture of the AAE's encoder and decoder can be asymmetric, and the degree of asymmetry can be adjusted based on the computation and processing abilities of the transmitting node, the available bandwidth, and the performance requirements of a specific downstream task. In the proposed framework, the encoder and decoder are jointly trained, enabling the system to extract downstream task-related information from one or more time series inputs. Verified for Human Activity Recognition (HAR), the framework demonstrates effective feature compression while maintaining efficient task performance.

This article introduces an Unmanned Aerial Vehicle - enabled content management architecture that is suitable for critical content access in communities of users that are communication-isolated during diverse types of disaster scenarios. The proposed architecture leverages a hybrid network of stationary anchor UAVs and mobile Micro-UAVs for ubiquitous content dissemination. The anchor UAVs are equipped with both vertical and lateral communication links, and they serve local users, while the mobile micro-ferrying UAVs extend coverage across communities with increased mobility. The focus is on developing a content dissemination system that dynamically learns optimal caching policies to maximize content availability. The core innovation is an adaptive content dissemination framework based on distributed Federated Multi-Armed Bandit learning. The goal is to optimize UAV content caching decisions based on geo-temporal content popularity and user demand variations. A Selective Caching Algorithm is also introduced to reduce redundant content replication by incorporating inter-UAV information sharing. This method strategically preserves the uniqueness in user preferences while amalgamating the intelligence across a distributed learning system. This approach improves the learning algorithm's ability to adapt to diverse user preferences. Functional verification and performance evaluation confirm the proposed architecture's utility across different network sizes, UAV swarms, and content popularity patterns.

This paper presents a Micro-Unmanned Aerial Vehicle (UAV)-enhanced content management system for disaster scenarios where communication infrastructure is generally compromised. Utilizing a hybrid network of stationary and mobile Micro-UAVs, this system aims to provide crucial content access to isolated communities. In the developed architecture, stationary anchor UAVs, equipped with vertical and lateral links, serve users in individual disaster-affected communities. and mobile microferrying UAVs, with enhanced mobility, extend coverage across multiple such communities. The primary goal is to devise a content dissemination system that dynamically learns caching policies to maximize content accessibility to users left without communication infrastructure. The core contribution is an adaptive content dissemination framework that employs a decentralized Top-k Multi-Armed Bandit learning approach for efficient UAV caching decisions. This approach accounts for geo-temporal variations in content popularity and diverse user demands. Additionally, a Selective Caching Algorithm is proposed to minimize redundant content copies by leveraging inter-UAV information sharing. Through functional verification and performance evaluation, the proposed framework demonstrates improved system performance and adaptability across varying network sizes, micro-UAV swarms, and content popularity distributions.

This paper proposes a learning-driven approach for medium access slot allocation in the presence of malicious nodes. Learning policies are developed with the goal of defending against several forms of quasi-random slot-scheduling attack models used by the malicious nodes. The primary learning objective for the non-malicious nodes is to minimize the degradation in network performance caused by the malicious nodes. This is accomplished while minimizing the bandwidth share of the malicious nodes. These objectives are achieved using a Multi-Armed Bandit (MAB) learning architecture that allows the nodes to learn transmission schedule on-the-fly, and without the need for any central arbitrator. Two different scheduling policies are introduced: robust and reactive policies. Following the design, a detailed characterization of these policies and their use in different application-specific scenarios are presented. An analytical model of the system is developed to find the benchmark throughput for different malicious attack models. It is demonstrated that the proposed framework allows network nodes to learn close-to -benchmark slot scheduling, while thwarting attacks from the malicious nodes. The proposed architecture is validated for various mesh networks and traffic conditions in the presence of different attack models enacted by the malicious nodes.

This paper proposes an unmanned aerial vehicle (UAV) aided content management system in communication-challenged disaster scenarios. Without cellular infrastructure in such scenarios, community of stranded users can be provided access to situation-critical contents using a hybrid network of static and traveling UAVs. A set of relatively static anchor UAVs can download content from central servers and provide content access to its local users. A set of ferrying UAVs with wider mobility can provision content to users by shuffling them across different anchor UAVs while visiting different communities of users. The objective is to design a content dissemination system that on-the-fly learns content caching policies for maximizing content availability to the stranded users. This paper proposes a decentralized Top-k Multi-Armed Bandit Learning model for UAV-caching decision-making that takes geo-temporal differences in content popularity and heterogeneity in content demands into consideration. The proposed paradigm is able to combine the expected reward maximization attribute and a proposed multidimensional reward structure of Top-k Multi-Armed Bandit, for caching decision at the UAVs. This study is done for different user-specified tolerable access delay, heterogeneous popularity distributions, and inter-community geographical characteristics. Functional verification and performance evaluation of the proposed caching framework is done for a wide range of network size, UAV distribution, and content popularity.

This paper presents a lightweight on-device liquid consumption tracking system based on a semi-supervised learning paradigm. The online learning framework caters to scenarios where a hydration tracking bottle/device has no prior knowledge of a user's consumption gesture patterns. The proposed iterative semi-supervised learning (ISSL) framework uses sparsely labelled user gesture events acquired from the IMU sensors installed on a bottle, such that it can learn to differentiate between sip and non-sip gestures by specific individuals. Two different strategies, namely, population-based, and distance-based, are employed to achieve the desired clustering performance. A comparative study between these strategies has been presented in terms of clustering accuracies for classifying sip and non-sip gestures. The proposed architecture is shown to be lightweight in terms of computation complexity and memory usage of the bottle-embedded hardware. The trade-off between classification accuracy and computation complexity is analyzed for different algorithmic hyper-parameters and it is shown how to manage this trade-off. Extensive experimentation and simulation study has been conducted for multiple users' drinking patterns to validate the proposed learning paradigm.

This article proposes an unmanned aerial vehicle (UAV) aided content provisioning system in communication-challenged disaster scenarios. In such scenarios, without the availability of static base stations and their wireline backhauls, community of stranded users can access contents from a network of static and traveling UAVs. A set of relatively static anchor UAVs with vertical as well as lateral links can provide content access to its local users. A set of ferrying UAVs with only lateral links, but with wider mobility, can provision content to users while visiting different communities of users. The objective is to design a content dissemination system that handles user demand heterogeneity while maximizing content availability to the requesting users. This work proposes a popularity-based caching policy which tackles heterogeneity in content popularity across a disaster region. A novel value-based caching policy is developed which considers the popularity and the tolerable access delay of the content requests from the users to make caching decisions. The paper develops a novel approach called Joint Deployment of Ferrying UAVs (JDFU) Algorithm to exploit the collective storage of ferrying UAVs which boosts content distribution for users. Through analytical modeling and simulation experiments it is demonstrated that content availability can be maximized by choosing an optimal cache storage segmentation factor, JDFU configuration and hover time of ferrying UAVs. This analysis is done for different user-specified tolerable access delay, heterogeneous popularity distributions and intercommunity geographical characteristics. The paper does functional verification and performance evaluation of the proposed caching framework under a wide range of network size, UAV distribution, content popularity, and ferrying UAV trajectories.

This article presents an unmanned aerial vehicle (UAV) based caching framework for content provisioning in disaster scenarios. In a disaster scenario without the availability of static base stations and their wireline backhauls, community of stranded users can access contents from a network of static and traveling UAVs. A set of relatively static anchor UAVs with vertical as well as lateral links provide content access to its local users. A set of ferrying UAVs with only lateral links, but with wider mobility, can also provision content to users while visiting different communities of users. The algorithmic objective is to intelligently cache contents in the storage-constrained UAVs in order to maximize content availability for the users affected by such disasters. The paper develops a novel approach of content duplication within the anchor UAVs along with a mechanism to distribute non-duplicated contents across the ferrying UAVs. Through analytical modeling and simulation experiments it is demonstrated that content availability in such an arrangement can be maximized by choosing an optimal level of duplication for content with specific popularity distributions. The paper does functional verification and performance evaluation of the proposed caching framework under a wide range of network size, UAV distribution, content popularity, and ferrying UAV trajectories.

This paper presents a distributed framework for Medium Access Control (MAC) slot allocation in time-asynchronous wireless networks using Multi-Armed Bandits (MAB) based learning. MAC slot allocation is formulated as an MAB problem where the nodes act as independent learning agents and learn transmission policies that ensure collision free transmissions. A novel concept of Hysteretic MAB has been introduced to speed up learning convergence. In order to reduce the bandwidth overhead while maintaining a desired MAB learning speed, a novel slot defragmentation mechanism is introduced. Not relying on network time synchronization makes the proposed mechanism feasible for low-complexity and low-cost transceivers for wireless sensor and loT networks. The proposed mechanism is tested and evaluated on both fully connected and arbitrary mesh network topologies and is shown to be scalable with network size and topological degree. It is also shown that in partially connected topologies, the mechanism learns spatial channel reuse, thus leading to better spectral usage efficiency.

This paper demonstrates a strategy for improving aggregate (i.e.: multiple drink) water consumption estimates obtained from a bottle-attachable IMU sensor through heuristic fusion. Aggregate consumption is estimated based upon residual container volume using a Gaussian process regression model trained on over 1,500 drinks. The model estimates the fill level of the bottle using hand-engineered features describing the estimated inclination during drinking. Fill level estimates are fused with an empirically parameterized heuristic consumption model. For initial proof-of-concept, fusion is performed using complementary and Kalman filtering. Both techniques are evaluated for 32 dedicated testing experiments containing 12 drinks each. Root mean square fill ratio estimation errors are reduced by 17.3% and 39.6% versus raw sensor estimates using the complementary and Kalman fusion frameworks, respectively.

This paper presents a novel mechanism for implementing Chaotic Pulse Position Coded Protocol data units (CPPCPs) for networking in sensor and IoT networks with thin energy budgets. The core idea of CPPCP is to encode a protocol data unit (PDU) in terms of Ultra-Wide Band (UWB) pulse train with chaotically varied inter-pulse intervals. The proposed CPPCP architecture fulfills the communication security by achieving higher randomness between different data symbols, lower probability of unambiguity, and significant energy savings by using a smaller number of pulse transmissions than the existing chaotic coding schemes in the literature. A concrete analysis for the implementation of CPPCP is provided based on the variation of pulse shape and position caused by specific hardware characteristics. A prototype sensor platform is designed for the implementation of CPPCP. Extensive simulation results are presented for evaluating the performance of the proposed architecture.

This paper develops a brand new approach towards data transfer using position-modulated pulses. The core idea is to encode a data value in terms of the time gap between two transmitted pulses whose positions are modulated accordingly. It is shown that this concept allows sending a data value using significantly less number of pulses or logical bits as used in conventional packet-based transports. Significant savings may come from the fact that it does not require the large number of preamble bits needed for synchronization in packets. Fewer pulses can lead to reduced-energy overhead compared to packets. The framework is mainly targeted towards sensing applications in which energy is a premium. Building on this fundamentally new concept, methods are developed in this paper for: 1) pulse-based transport on transmit-only links, and 2) code compression to reduce the overall energy and transmission delay. Analytical models and extensive simulations have been performed for evaluating the performance of the system relative to comparable packet-based solutions.

Majority of existing Delay Tolerant Network (DTN) multicast routing approaches attempt to individually optimize performance indices such as message delay, forwarding cost, and required storage. Very few define composite cost factors by combining multiple such indices into a single index. This paper introduces a composite index, namely economic gain, which combines revenue from delivery and forwarding cost from disseminating commercial content such as coupons. In this context, an Economy Driven Content Dissemination (EDCD) protocol is proposed that controls D2D content dissemination with an end goal of maximizing the economic gain for a coupon-generator. Using the DTN simulation software ONE, we run experiments for functional validation and performance evaluation of the proposed protocol with respect to few other competitive protocols under different mobility scenarios.

The proposed architecture implements a content sharing infrastructure using device-to-device wireless links towards the goal of reducing the usage cost of 3G/4G cellular links. For downloading digital content, a device can first search within the local mobile network at the edge for the requested content before downloading it from the CP's server. The expected usage of 3G/4G bandwidth in this approach can be lower. Building on a content pricing and sharing incentive structure, the paper first develops a new heterogeneous model for content request from mobile devices. Using this model, it then develops an optimal cooperative caching strategy with the goal of minimizing the content provisioning cost in wireless mobile networks. Using the mobile network simulator ONE, it is shown that the proposed mechanism is able to reduce bandwidth usage and the resulting content provisioning cost compared to traditional caching mechanisms in both monolithic and community-based mobility scenario.

This paper proposes a Q-learning based Device-to-Device multicast routing framework for Social Wireless Networks. The goal of the proposed Scalable Q-learning based Gain-aware Routing (SQGR) content dissemination algorithm is to maximize a predefined economic gain for commercial content generators. This economic gain is defined as the revenue from delivery of a coupon minus the forwarding cost associated with that delivery. SQGR, with its embedding learning abilities, is expected to be robust in dynamic mobility environments. It also preserves scalability and privacy since it does not require storage of per-individual consuming interest and interaction profiles within the network. Using the DTN simulator software ONE, we evaluate functional validity and compare gain performance of SQGR with few existing protocols under various commercial, network and protocol parameters.

This paper develops an incentive based cooperative content caching framework for Social Wireless Networks (SWNETs) in which content demands are hierarchically heterogeneous. The heterogeneous request model incorporates user preference for different categories/genres, and contents under each category, both following power law distributions at local as well as global levels. Based upon such request generation model, an optimal incentive based Heterogeneous Split Caching algorithm is proposed which can minimize electronic content provisioning cost using cooperative caching policies. Detailed simulation experiments for functional validation and performance evaluation with respect to traditional caching strategies are then performed under various protocol and network parameters. The baseline HSC mechanism is also tested against a special benchmark situation that provides a steady state performance bound, which can occur after an infinite request horizon.

This paper presents an ultrasound echolocation-based approach for human activity recognition in indoor settings. The key novelty of the proposed approach is to perform activity analysis using distance estimated through “first-reflection echolocation”. The distance to the nearest obstructing object is computed using the first reflected ultrasound signal. All subsequent reflected signal components from other distant objects are ignored. This leads to an extremely simple signal (i.e., time-series distance data) analysis approach with very low computational complexity. Especially so, when compared with the existing approaches in literature in which full reflected signal analysis, often with Doppler Shift computation, is performed for activity classification. It is demonstrated that for the goal of isolating workplace sedentary behavior, the proposed approach can differentiate between sitting, standing, and walking (i.e., in-office pacing) with more than 80% accuracy. This was validated with different classifiers applied on data collected from multiple subjects in multiple sessions. Recorded video was used as the ground-truth for training the classifiers.

This paper presents a mechanism to use Traffic Analysis (TA) for identifying sources of tunneled video streaming traffic. The key idea is to probe encrypted and tunneled video streaming traffic at a client-side firewall so that the firewall can identify the traffic source using traffic analysis, and block or throttle traffic from that particular source. The key contribution in this paper is to evaluate Packet Arrival Interval (PAI) as a classification feature for identifying sources of tunneled video streaming traffic. Using OpenVPN servers for creating encryption tunnels, experiments are conducted for a large number of popular video steaming servers and multiple client devices located in different geographical locations. It was experimentally demonstrated that using PAI as a classification feature it is indeed possible to identify video streaming sources with high accuracy and low false-positive rates. The paper also does a thorough analysis of PAI to reveal that the feature embeds two very important classification sub-features, namely, Inter Packet Delay Variation (IPDV), and Inter Packet Generation Delay (IPGD), Classification is then performed using those two sub-features to demonstrate their abilities for video source identification.

The use of compensatory motion strategies amongst stroke sufferers has been well documented in the literature. While these modified movement patterns allow individuals to address functional deficits, research suggests that employing such techniques may inhibit motor skill recovery. Although detection of these movements using either wearable sensors or gaming technologies within home-based rehabilitation regiments has been demonstrated, both the physical limitations and technological preferences of the target population limit the efficacy of such solutions. The objective of this extended abstract is to demonstrate progress towards employing a contactless first-reflection ultrasonic echolocation sensor to detect compensatory movements associated with excessive trunk flexion in response to reduced upper extremity functionality. Results from preliminary experiments in which compensatory motions are induced using a motion-restricting elbow brace are described herein. Preliminary results are promising, with average classification accuracy exceeding 78%.

This paper presents a multicast Delay Tolerant Network (DTN) routing protocol that uses an economic gain based routing utility for device-to-device (D2D) commercial coupon dissemination. Majority of the existing DTN routing approaches are not practically applicable to commercial content dissemination since they attempt to individually optimize performance indices such as message delay, forwarding cost, and required storage, but not their combined economic benefits. This paper uses a composite routing performance index, namely economic gain, which combines revenue from delivery and forwarding cost from disseminating commercial content such as coupons. It then develops a new gain-aware DTN multicast routing utility that is designed around the notion of consumption interest and coupon redemption probability. We propose two routing protocols, namely, Predictive Gain Utility Routing Individual (PGUR-I), and a more scalable version, Predictive Gain Utility Routing Aggregated (PGUR-A), to control D2D coupon dissemination with an end goal of maximizing the economic gain for a coupon-generator. Using the DTN simulation software ONE, we run experiments for functional validation and performance evaluation of the proposed protocols with respect to an existing competitive protocol under different protocol constraints and mobility scenarios.

This paper develops an energy-aware and through- substrate sensor network using a Pulse Switching (PS) architecture for Structural Health Monitoring applications. Pulse Switching based protocols as developed in our prior work use single pulses instead of multi-bit packets for information delivery with ultra high energy-efficiency. Such packet-less networking is shown to be sufficient for event monitoring applications with binary sensing requirements. Pulse Switching using ultrasound, in particular, is well suited for communication through metal/composite substrates used in structures such as bridges, aircraft wings, etc. This paper presents a joint MAC- Routing architecture and its associated algorithms for Pulse Switching using an ultrasonic through- substrate physical layer. It also develops novel energy-aware protocol syntaxes in the PS domain for reliable operation in intermittently energy- constrained systems such as those powered by vibration energy harvesting. Experimental evaluation of a through-substrate pulse modem and ultrasonic pulse data-link is presented for demonstrating the feasibility of a pulse based ultrasonic physical layer. Using simulation experiments, it is shown that the proposed energy- aware mechanisms can offer a performance-enhanced through-substrate network that can be reliably used for structural health monitoring using energy harvested from structure vibrations.

This paper explores the feasibility of evolving Medium Access Control (MAC) protocols using evolving finite state machines. Network protocols formulated as genotypes are evaluated through an evaluation framework and made to compete under the pressure of a desired performance fitness. Specifically, the evolution of two well-known protocols, Slotted-ALOHA and Carrier Sense Multiple Access (CSMA) is explored. Using a C based protocol fitness evaluator and a Java based evolution fabric, we show that a generalized state machine is able to evolve complex MAC behavior when time or channel sensing modalities are provided to the network nodes. We show that protocol synthesis takes place under a wide range of network parameters, loading conditions as well as limited/full evolutionary degrees of behavioral freedom. Performed adaptability experiments prove feasibility of the framework to adapt with runtime changes of single or multiple system parameters. The ultimate goal of this research is to develop an alternative design paradigm for protocols in heterogeneous network and traffic scenarios.

This paper explores a framework of Medium Access Control (MAC) protocol synthesis using evolving finite state machines. A network protocol is coded as a genotype and its resulting state machine behavior manifests in the form of the corresponding phenotype, leading to specific protocol performance. The genotype or state machine is allowed to evolve under the pressure of desired performance fitness. In this paper, the proposed framework is applied specifically to the well-known pure-ALOHA protocol for which protocol performance is known under a certain network and loading conditions. Using a C based protocol fitness evaluator and a Java based evolution fabric, we show that a generalized state machine is able to evolve towards known pure-ALOHA solutions under a wide range of network and loading conditions.

This paper presents mechanisms for identification of web traffic masqueraded behind encrypted Virtual Private Network (VPN) tunnels. Website identification using Traffic Analysis (TA) has many administrative applications including preventing access to forbidden websites and site-specific Quality of Service (QoS) provisioning. Previous works in this area mainly looked at the problem of identifying traffic from relatively static websites, thus limiting the applicability of the technique for websites with dynamically changing contents. In this work, we attempt to generalize the mechanism for dynamic sites by the way of introducing a new classification feature traffic surge period, and adapting the first n Components of Haar Wavelet Transformation, which is commonly used in traditional signal processing applications. Our results from fingerprinting experiments carried out over an SSL VPN shows that the addition of these new features can indeed bridge the fingerprinting performance gap between static and dynamic websites.

This paper presents a new way of providing privacy for Internet of Things (IoT) in a multi-trust-domain environment. The key idea is to develop a privacy-aware slotted channel access mechanism using which IoT nodes from multiple operators or trust domains can share wireless channel without mutually exposing their identities, thus alleviating threats from cross-trust-domain traffic analysis geared toward node-profiling, link layer topology estimation, node-tracking, and flow-tracking. The proposed scheme uses a novel zero-exposure slot allocation scheme in which packet transmission timing is the only information that is used for scheduling, collision detection, and collision resolution purposes. In addition to the proposed access scheme, this paper reports the design of a custom hardware unit for implementing the proposed protocol in a test-bed of sensors, emulating IoT networks. Presented results include functional validation and performance of the proposed channel access while preventing complete cross-trust-domain identity exposure.

This paper presents a conceptual framework of social community based cooperative caching for minimizing electronic content provisioning cost in Mobile Social Wireless Networks (MSWNET). Drawing motivation from Amazon's Kindle electronic book delivery model, this paper develops practical network, service, and pricing models which are then used for creating an optimal cooperative caching strategy based on social community abstraction in wireless networks.

This paper presents a novel pulse switching protocol framework for ultra light-weight wireless network applications. The key idea is to abstract a single Ultra Wide Band (UWB) pulse as the information switching granularity. Pulse switching is shown to be sufficient for on-off style event monitoring applications for which a monitored parameter can be modeled using a binary variable. Monitoring such events with conventional packet transport can be prohibitively energy-inefficient due to the communication, processing, and buffering overheads of the large number of bits within a packet's data, header, and preambles for synchronization. The paper presents a joint MAC-routing protocol architecture for pulse switching with a novel hop-angular event localization strategy. Through analytical modeling and simulation-based experiments it is shown that pulse switching can be an effective means for event networking, which can potentially replace the traditional packet transport when the information to be transported is binary in nature.

This paper introduces cooperative caching policies for minimizing electronic content provisioning cost in Social Wireless Networks (SWNET). SWNETs are formed by mobile devices, such as data enabled phones, electronic book readers etc., sharing common interests in electronic content, and physically gathering together in public places. Electronic object caching in such SWNETs are shown to be able to reduce the content provisioning cost which depends heavily on the service and pricing dependences among various stakeholders including content providers (CP), network service providers, and End Consumers (EC). Drawing motivation from Amazon's Kindle electronic book delivery business, this paper develops practical network, service, and pricing models which are then used for creating two object caching strategies for minimizing content provisioning costs in networks with homogenous and heterogeneous object demands. The paper constructs analytical and simulation models for analyzing the proposed caching strategies in the presence of selfish users that deviate from network-wide cost-optimal policies. It also reports results from an Android phone-based prototype SWNET, validating the presented analytical and simulation results.

This paper presents the design and implementation of a machine learning based activity classification mechanism for hens using a wearable sensor system. Legislation and social demands in the U.S. and Europe are pushing the poultry industry towards the usage of non-cage housing systems. However, non-cage systems typically house hens in groups of hundreds or thousands, which makes it nearly impossible for caretakers to visually assess the health, welfare, or movement of individual hens or to follow a particular hen over time. In the study, laying hens were fitted with a lightweight (10 g) wireless body-mounted sensor to remotely sample activity data. Specific machine learning mechanisms are used on the features extracted from activity data to identify a target set of activities of the hens. The paper establishes technological feasibility of using such body-mounted sensor systems for accurate hen activity monitoring in a non-cage housing system.

This paper proposes a distributed TDMA slot allocation protocol that relies on absolutely no information exchange among the participating nodes. This novel property allows the protocol to work in restricted anonymous environments such as in privacy-sensitive body area networks and various military networks in which nodes may need to cooperate in distributed TDMA but are not allowed to explicitly exchange any information such as node-IDs in order to preserve their anonymity. This paper introduces an innovative approach of time-coded packet transmissions for implicitly exchanging slot timing information. It is shown that using such implicit information, together with a notion of interrupt control packets, the nodes are able to self-allocate collision-free TDMA slots in an anonymous manner. The protocol is evaluated and its performance has been shown using extensive simulation models.

This paper presents a novel energy-efficient pulse switching protocol for ultra-light-weight cellular sensor network applications. The key idea is to abstract a single pulse, as opposed to multi-bit packets, as the information exchange mechanism. Pulse switching is shown to be sufficient for event monitoring applications with binary sensing in terms of cellular localization. Event monitoring with conventional packet transport can be prohibitively energy-inefficient due to the communication, processing, and buffering overheads of the large number of bits within a packet's data, header, and preambles. The paper presents a joint MAC and Routing architecture for pulse switching with novel cellular event localization. Through simulation experiments, it is shown that pulse switching can be an effective means for event based networking, which can potentially replace packet transport when the information to be transported is binary in nature.

Dynamic Spectrum Access can enable a secondary user in a cognitive network to access unused spectrum, or whitespace, found between primary user transmissions in a wireless network. The key design objective for a secondary user access strategy is to "scavenge” the maximum amount of spatio-temporally fragmented whitespace while limiting the amount of disruption caused to the primary users. In this paper, we first measure and analyze the whitespace profiles of an 802.11 network (using ns-2 simulation) and a non-802.11 (CSMA)-based network (developed on TelosB Motes). Then we propose two novel secondary user access strategies, which are based on measurement and statistical modeling of the whitespace as perceived by the secondary users. Afterward, we perform simulation experiments to validate the effectiveness of the proposed access strategies under single and multiple secondary user scenarios, and evaluate their performance numerically using the developed analytical expressions. The results show that the proposed access strategies are able to consistently scavenge between 90 and 96 percent of the available whitespace capacity, while keeping the primary users disruption less than 5 percent.

This paper proposes a distributed TDMA slot allocation protocol that relies on zero amount of explicit information exchange among the participating nodes. This novel property allows the protocol to work in restricted environments such as in privacy-sensitive body area networks and various military networks in which nodes may need to cooperate in distributed TDMA but are not allowed to explicitly exchange any information in order to preserve their anonymity. This paper introduces an innovative approach of time-coded packet transmissions for implicitly exchanging slot timing information. It is shown that using such implicit information, together with a notion of pattern based shadow packet, the nodes are able to self-allocate collision-free TDMA slots in a Zero Exposure manner. The protocol is evaluated and its performance has been shown using extensive simulation models.

This paper presents a novel energy-efficient pulse switching protocol for ultra light-weight wireless network applications. The key idea is to abstract a single pulse, as opposed to multi-bit packets, as the information exchange mechanism. Pulse switching is shown to be sufficient for event sensing applications with binary sensing. Event sensing with conventional packet transport can be prohibitively energy-inefficient due to the communication, processing, and buffering overheads of the large number of bits within a packet's data, header, and preambles. The paper presents a joint MAC-Routing architecture for pulse switching with a novel hop-angular event localization. Through simulation experiments, it is shown that pulse switching can be an effective means for event based networking, which can potentially replace the packet transport when the information to be transported is binary in nature.

Cooperative Caching can be an effective mechanism for reducing electronic content provisioning cost in Social Wireless Networks (SWNETs) which are formed by a collection of mobile data enabled phones physically gathering in settings such as university campus, malls, airport and other public places. In this paper, we first propose an optimal collaborative object caching policy in order to minimize the object provision cost in SWNETs with homogenous user requests and a peer-rebate model for promoting collaboration. Then using an analytical model we study the impacts of user selfishness on the provisioning cost and the earned rebate when certain nodes in an SWNET selfishly deviate from the optimal policy in order to increase their individually earned rebate. The analytical model is extensively validated by experimental results from simulated SWNETs using the network simulator ns2.

Firewalls are one of the essential security elements to enforce access policies in computer networks. Open network architecture, shared wireless medium, stringent resource constraints, and highly dynamic network topology impose a new set of challenges on deploying firewalls in a mobile wireless environment. The current state-of-the-art demands for self protection by personal (i.e. local) firewalls for each node; however, this requires that all unwanted traffic travels all the way to the node before it is discarded at the destination. This wastes considerable bandwidth and power of all of the nodes in a network with multi-hop routing, specially if a node is under a denial of service (DoS) attack. In this paper, we develop a novel distributed firewalling scheme for wireless networks in which nodes collaboratively perform packet filtering to address resource squandering. The proposed scheme introduces techniques to distribute discarding rules based on both proactive and reactive routing protocols. It also proposes efficient rule placement mechanisms to maximize the number of packets discarded remotely before they reach the destination and minimize the number of unwanted packet forwardings. The scheme is evaluated through various simulation scenarios. The simulation results show that by distributing only 1% of the rules, about 42% of the unwanted traffic is discarded before it reaches the destination, which significantly saves the network resources. Saving about 30% of the wasted bandwidth can be crucial for the performance of a wireless network.

This paper presents a conceptual framework of mobility-aware cooperative caching for minimizing electronic content provisioning cost in Mobile Social Wireless Networks (MSWNET). The MSWNETs are formed by wireless mobile devices sharing common interests in electronic content, and physically gathering in various public settings such as University campuses, work places, malls, and airports. Cooperative caching in such MSWNETs are shown to be able to reduce the content provisioning cost which depends heavily on the service and pricing dependencies among various stakeholders including the content provider, the network service provider, and the end consumers. This paper first develops practical network, search, and pricing models which are then used for creating an optimal cooperative caching strategy for static networks. In addition to proving its optimality for the static case, the paper constructs analytical models, simulation, and prototype experiments for evaluating the performance of the proposed strategy. Building on the static case, the paper then develops a mobility analysis technique and demonstrates the applicability of the proposed scheme in the presence of user mobility.

This paper presents a novel energy-efficient pulse switching protocol for ultra light-weight wireless network applications. The key idea is to abstract a single pulse, as opposed to multi-bit packets, as the information exchange mechanism. Pulse switching is shown to be sufficient for event and target tracking applications with binary sensing. Target tracking with conventional packet transport can be prohibitively energy-inefficient due to the communication, processing, and buffering overheads of the large number of bits within a packet's data, header, and preambles. The paper presents a joint MAC and Routing architecture for pulse switching with a novel hop-angular event localization. Through analytical modeling and simulation experiments, it is shown that pulse switching can be an effective means for event based networking, which can potentially replace the packet transport when the information to be transported is binary in nature.

Abstract This paper presents a distributed and localized interference‐aware channel assignment framework for multi‐radio wireless mesh networks in a cognitive network environment. The availability of multiple interfaces and channels in wireless devices is expected to enhance network throughput in wireless mesh networks. A notable design issue in such networks is how to dynamically assign available channels to multiple radio interfaces for maximizing effective network throughput by minimizing interference. The proposed framework uses a novel interference estimation method by utilizing distributed conflict graphs on a per‐interface basis. Presented results obtained via simulation studies in 802.11 based multi‐radio mesh networks indicate that for both homogeneous and heterogeneous primary networks, the proposed protocol can facilitate a large increase in network throughput in comparison with a Common Channel Assignment mechanism that is used as a benchmark in the literature. Copyright © 2010 John Wiley & Sons, Ltd.

This paper presents a history based statistical channel access mechanism for enabling traffic prioritization in wireless sensor networks. Prioritized access is realized such that low priority non-real-time sensors can access channel bandwidth that is unused by high priority real-time traffic. The key idea is for the low priority sensor nodes to first observe and statistically model the channel usage pattern by the high priority traffic. Then make probabilistic transmissions depending on the amount of time elapsed after the most recent high priority packet transmission ends. The objective is to dimension such probabilities based on the channel utilization statistics so that the non-priority traffic throughput is maximized while protecting the high-priority traffic from disruptions. The proposed access mechanism is implemented in a TelosB mote based sensor testbed in which the non-priority motes continually measures the RSSI to infer the channel usage pattern and probabilistically access the channel while different types of traffic is sent by high-priority TelosB motes. Experimental results from the testbed demonstrates that the proposed mechanism can improve non-priority traffic throughput by up to approximately 70% over compared protocols, while limiting the disruptions to high-priority traffic to pre-specified bounds of 3% to 5%.

This paper introduces an optimal cooperative caching policy for minimizing electronic content provisioning cost in Social Wireless Networks (SWNETs). The SWNETs are typically formed by a collection of mobile devices, such as data enabled phones, net-books, electronic book readers etc., sharing common interests in electronic content, and physically gathering in settings such as University campuses, work places, malls, airports, train stations and other public places. Electronic object caching in such SWNETs are shown to be able to reduce the content provisioning cost which depends heavily on the service and pricing dependencies among various stakeholders including the content provider, the network service provider, and the end consumers. Drawing motivation from Amazon's Kindle electronic book delivery business, this paper develops a practical network, service, and pricing model which are then used for creating the proposed optimal caching strategy. In addition to proving the optimality of the mechanism, the paper constructs extensive analytical and simulation models for analyzing the proposed caching strategy and its optimal operating points for the mobile ecosystem stakeholders.

Dynamic Spectrum Access in a Wireless LAN can enable a set of secondary users' devices to access unused spectrum, or whitespace, which is found between the transmissions of a set of primary users' devices. The primary design objective for an efficient secondary user access strategy is to be able to “scavenge” spatio-temporally fragmented bandwidth while limiting the amount of interference caused to the primary users. In this paper, we propose a secondary user access strategy which is based on measurement and modeling of the whitespace as perceived by the secondary users in a WLAN. A secondary user monitors and models its surrounding whitespace, and then accesses the available spectrum so that the effective secondary throughput is maximized while the resulting interference to the primary users is limited to a pre-defined bound. We first develop analytical expressions for the secondary throughput and primary interference, and then perform ns2 based simulation experiments to validate the effectiveness of the proposed access strategy, and evaluate its performance numerically using the developed expressions. The results show that the proposed access strategies are able to consistently scavenge between 90% and 96% of the available whitespace bandwidth, while keeping the primary users disruption less than 5%.

This paper presents a cooperative object caching mechanism for maintaining high content availability in Social Wireless Networks. Most of the existing cooperative caching schemes in wireless networks provide either high network level availability or high node level availability, but not both at the same time. The first one ensures object availability in isolated network partitions and the second one ensures object availability in individual nodes when they are completely detached from the rest of the network. In this paper, we propose a novel cache partitioning mechanism that is able to provide high network level and high node level availabilities at the same time. This scheme reduces the generated network traffic compared to the prevalent schemes in the literature. In addition to computing theoretical bounds for the availabilities and generated traffic, caching performance is evaluated using a detailed ns2 simulation model under static and mobile networks with realistic mobility patterns.

This article presents a novel transmission power assignment mechanism for on-body wireless links formed between severely energy-constrained wearable and implanted sensors. The key idea is to develop a measurement-based framework in which the postural position as it pertains to a given wireless link is first inferred based on the measured RF signal strength and packet drops. Then optimal power assignment is done by fitting those measurement results into a model describing the relationship between the assigned power and the resulting signal strength. A closed loop power control mechanism is then added for iterative convergence to the optimal power level as a response to both intra-and-inter posture body movements. This provides a practical paradigm for on-body power assignment, which cannot leverage the existing mechanisms in the literature that rely on localization, which is not realistic for on-body sensors. Extensive experimental results are provided to demonstrate the model building and algorithm performance on a prototype body area network. The proposed mechanism has also been compared with a number of other closed loop mechanisms and an experimental benchmark.

Dynamic spectrum access can enable secondary network users to access unused spectrum, or whitespace, which is found between the transmissions of primary users in a wireless network. The main design objectives for secondary user access strategy are to be able to "scavenge" spatio-temporally fragmented whitespace opportunities while limiting the amount of interference caused to the primary users. In this paper, we propose a novel secondary user access strategy which is based on measurement and modeling of the whitespace as perceived by the secondary network users. A secondary user continually monitors its surrounding whitespace, models it, and then attempts to access the available spectrum holes so that the effective secondary throughput is maximized while the resulting interference to the primary users is limited to a pre-defined bound. We first develop analytical expressions for the secondary throughput and primary interference, and then perform ns2 based simulation experiments to validate the effectiveness of the proposed access strategy, and evaluate its performance numerically using the developed expressions.

This paper proposes a neighborhood route diffusion (NRD) mechanism in which certain selected entries from a node's routing table are selectively diffused to its neighbor nodes. When done efficiently, this selective route diffusion can create a temporary envelope of emergency route information to a destination around all nodes that are actively forwarding packets to that specific destination. When a link on a route fails due to mobility, the intermediate node on the failed link can forward packets to one of its neighbors which has already been diffused with the route information for the corresponding destination. It is shown that in most such occurrences, the packets can be successfully forwarded all the way to the destination using such pre-diffused routing information. This mechanism for salvaging packets during mobility-initiated link breaks can avoid packet drops which are usually prevalent in regular mobility aware routing protocols such as AODV. This paper presents strategies and network protocols for the selective NRD process and its associated routing mechanisms.

This paper presents an integrated sink navigation and data routing framework called network-assisted data collection (NADC) in which optimum trajectories for a mobile data harvester (MDH) are computed such that a desired balance between energy-efficiency and collection delay is achieved. We have formulated the problem of mobile sink based data collection using a configurable knob (k) to find the desired balance. The key concept is to appropriately adjust the data routing and mobile sink trajectory by varying parameter k. The paper introduces a large slew of application scenarios with their unique optimization objectives, and demonstrates the relevance of the proposed framework in each such scenario. The notable solution components of NADC include a fully distributed and network assisted MDH navigation mechanism that does not rely on sensor localization services, and a multi-hop routing mechanism which works with the underlying sink navigation so that the sensor data can be aggregated and uploaded through dynamically chosen gateway nodes located around the sink trajectory.

This paper presents a novel architectural solution to address the problem of scalable routing in very large sensor networks. We develop a routing solution off-network control processing (ONCP) that achieves control scalability in large sensor networks by shifting certain amount of routing functions to an ldquooff-networkrdquo server. A tiered and hybrid routing approach, consisting of ldquocoarse grainrdquo global routing, and distributed ldquofine grainrdquo local routing is proposed for achieving scalability by avoiding network wide control message dissemination. We present the ONCP architectural concepts and analytically characterize its performance in relations to both flat and hierarchical sensor routing architectures. We also show ns2 based experimental results indicating that for large sensor networks with realistic data models, the packet drop, latency and energy performance of ONCP can be significantly better than those for flat and cluster-based protocols.

This paper presents a network-assisted sink navigation mechanism in which mobile sink trajectories are computed by a sensor network for energy-efficient data collection. The key idea is that for data collection, a mobile sink (referred to as Mobile Data Harvester MDH) should visit all network nodes in order to avoid energy- intensive multi-hop routing. While incurring higher data collection delays compared to the traditional static sink based collection, the proposed harvesting framework can deliver the maximum achievable lifetime for delay non-critical sensor applications. The main issue in this approach is to compute MDH trajectories that can reduce the average data collection delay while traversing through the radio ranges of all nodes, but not necessarily pass through the close proximity of each node. Our primary contribution is the design of a fully distributed and network assisted MDH navigation mechanism where MDH trajectories are cooperatively computed by the nodes without sensor localization services. Performance results obtained from an ns2 simulator indicate that the distributed algorithms and network assisted navigation framework can achieve low average delay, low delay variation and complete collection coverage of the network. Further, the performance of the distributed algorithms was found to be comparable to that of centrally computed solution.

This paper presents a self-organizing medium access control (MAC) protocol framework for distributed sensor networks with arbitrary mesh topologies. The novelty of the proposed In-band self-organized MAC (ISOMAC) protocol lies in its in-band control mechanism for exchanging time-division multiple access (TDMA) slot information with distributed MAC scheduling. A fixed-length bitmap vector is used in each packet header for exchanging relative slot timing information across immediate and up to two-hop neighbors. It is shown that, by avoiding explicit timing information exchange, ISOMAC can work without networkwide time synchronization, which can be prohibitive for severely cost-constrained sensor nodes in very large networks. A slot-clustering effect, caused by in-band bitmap constraints, enables ISOMAC to offer better spatial channel reuse compared to traditional distributed TDMA protocols. ISOMAC employs a partial node wake-up and header-only transmission strategy to adjust energy expenditure based on the instantaneous nodal data rate. Both analytical and simulation models have been developed for characterizing the proposed protocol. Results demonstrate that, with in-band bitmap vectors of moderate length, ISOMAC converges reasonably quickly, that is, approximately within a four to eight TDMA frame duration. Also, if the bitmap header duration is restricted within 10 percent of packet duration, then the energy penalty of the in-band information is quite negligible. It is also shown that ISOMAC can be implemented in the presence of network time synchronization, although its performance without synchronization is just marginally worse than that with synchronization.

This paper presents an architectural solution to address the problem of scalable routing in very large sensor networks. The control complexities of the existing sensor routing protocols, both node-centric and data-centric, do not scale very well for large networks with potentially hundreds of thousand of embedded sensor devices. This paper develops a routing solution off-network control processing (ONCP) that achieves control scalability in large sensor networks by shifting certain amount of routing functions to an "off-network" server. A tiered routing approach, consisting of "coarse grain" server based global routing, and distributed "fine grain" local routing is proposed for achieving scalability by avoiding network wide control message dissemination. We present the ONCP architectural concepts and characterize its performance using an ns2 based simulation model. Our experimental results indicate that for large sensor networks with realistic data models, the packet drop, latency and energy performance of ONCP can be significantly better than those for a well known sensor routing protocol directed diffusion.

This paper presents an architectural solution to address the problem of scalable routing for data intensive applications in very large sensor networks. Due to large routing overheads, the control complexity of the existing sensor routing protocols, both node-centric and data-centric, do not scale well in very large networks with potentially thousands of sensor devices. In this paper, we develop a hybrid architectural solution off-network control processing (ONCP) that achieves scalable routing in large networks by shifting certain amount of routing functions to an "off-network" routing server. A tiered routing approach is proposed to avoid network-wide control message dissemination. Our experimental results indicate that for large sensor networks with realistic data models, the packet drop, latency, energy performance and bandwidth usage of ONCP can be significantly better than those for completely distributed routing protocols such as directed diffusion.

This paper presents a performance comparison between the DSRC-recommended 802.11 Medium Access Control (MAC) and a novel Vehicular Self-Organizing MAC (VeSOMAC) protocol in the context Intelligent Transportation System applications. VeSOMAC is a distributed TDMA protocol that relies on in-band control exchange for autonomous and self-configurable TDMA slot allocation. Detailed network and vehicular traffic models have been developed for a Cooperative Collision Control (CCA) application operating in urban traffic intersection scenarios. Simulation results demonstrate that unlike the 802.11 style contention based protocols, VeSOMAC can offer better vehicle safety through smaller and bounded packet latency in vehicular ad hoc networks.

This paper presents a novel Medium Access Control protocol for inter-vehicular wireless networking using the emerging Dedicated Short Range Communication (DSRC) standards. The main contribution of the paper is the design of a self- configuring TDMA protocol capable of inter-vehicle message delivery with short and deterministic delay bounds. The proposed Vehicular Self-Organizing MAC (VeSOMAC) is designed to be vehicle location and movement aware so that the MAC slots in a vehicle platoon can be time ordered based on the vehicles' relative locations for minimizing the multi-hop delivery delay. A novel feature of VeSOMAC is its in-band control mechanism for exchanging TDMA slot information during distributed MAC scheduling. It is shown that by avoiding explicit timing information exchange, VeSOMAC can work without inter-vehicle time synchronization. The in-band control mechanism is also used for fast protocol convergence during initial network setup and topology changes due to vehicle movements. A simulation model has been developed for comparing VeSOMAC's performance with that of DSRC-recommended 802.11 MAC protocol for highway traffic safety applications.

This paper presents a mechanism for extending network life by introducing energy-aware mobility in wireless sensor networks. The concept of controlled mobility for energy saving has been motivated by a natural grouping behavior that is observed in Emperor penguins in the Antarctic region. As a survival strategy during the winters, large groups of Emperor penguins form a closely huddled group to improve their collective heat insulation. Individual penguins within a group exhibit a local mobility pattern that ensures that each individual spends equal amount of time at the periphery of the group, where the heat loss is the maximum. In this paper, we first draw a parallel between the heat loss of a penguin at the group periphery, and the routing energy burden of a sensor node near base stations. Then we develop a distributed and fully localized mobility control algorithm for collective extension of the network life. Experimental results demonstrate that the proposed controlled mobility can significantly extend a sensor network's operating life even in situations where the energy cost of physical node movement is modeled as high as up to four orders of magnitude larger than the energy cost for packet communication.

This paper presents a carrier-sense based transmission power control protocol, CSNE-PC, which addresses the energy inefficiency of basic power control protocols (BPC) for IEEE 802.11 based networks. In BPC, RTS and CTS packets are exchanged at full power while data and acknowledgement transmissions are carried out at low power. While being intuitive and simple to implement, BPC's energy efficiency is significantly degraded by a unique hidden collision problem, which is removed by the approach in this paper. In CSNE-PC, a node measures and analyses its carrier-sense activities for detecting 802.11 CTS packets in the extended neighborhood of carrier sense range. Upon detecting a CTS packet, the node sets or extends its MAC layer NAV and enters into a silence mode for the data packet duration. Unlike BPC, this enables CSNE-PC to control transmission power without having to suffer from additional data collisions. Evaluation of CSNE-PC through simulation demonstrates that under sustainable loading conditions, reduction in combined transmit and receive energy for CSNE-PC can be as high as 38%, and that is while retaining the throughput and delay characteristics of the regular 802.11 protocol

This paper proposes a broadcast based packet forwarding mechanism for intra-platoon cooperative collision avoidance (CCA) using dedicated short range communication (DSRC) links. The paper first motivates the needs for broadcast forwarding as opposed to unicast routing for transporting inter-vehicle data for safety-critical applications. Then it introduces an implicit acknowledgement mechanism for reducing the amount of broadcast traffic for enhanced packet delivery rate. We have developed a discrete event hybrid simulator InventSim, which is capable of jointly simulating DSRC based vehicular wireless networks, highway vehicle traffic with car following logic and various drivers' reaction models, and ITS application interface to the wireless network. Performance of the proposed broadcast forwarding for a CCA system has been characterized using InventSim. Reported results demonstrate that with inter-vehicle spacing of nearly one second, the proposed mechanism is capable of saving up to 90% of vehicles in a platoon from chain crashes following emergency events at the front of the platoon. The system has been also evaluated for a wide range of parameters including vehicle spacing, drivers' reaction time, and the amount of background non-CCA traffic

This paper presents a self-reorganizing slot allocation (SRSA) mechanism for TDMA based medium access control (MAC) in wireless sensor networks. With TDMA, a node can achieve significant energy savings by remaining active only during allocated slots for transmissions and receptions. In multi-cluster networks, it is often necessary for nodes to use either CDMA or FDMA for preventing interference across neighbor clusters. The goal of this paper is to provide an alternative design that can reduce inter-cluster TDMA interference without having to use spectrum expensive CDMA or FDMA. The primary contribution of this paper is to demonstrate that with adaptive slot allocation, it is possible to reduce such interference under low loading conditions, which is often the case for sensor networks with monitoring applications. The second contribution is to design a feedback based adaptive allocation reorganization protocol that can significantly reduce those interferences without relying on any global synchronization mechanisms. We present the design of SRSA and provide a simulation based characterization of the protocol in comparison with TDMA-over-CDMA, TDMA with random slot allocation and CSMA MAC protocols. The results indicate that with moderate cluster overlapping and low traffic, SRSA can significantly reduce inter-cluster TDMA interference while delivering TDMA-over-CDMA like energy efficiency, at the cost of higher delivery latency compared to TDMA-over-CDMA. Assuming its low complexity and narrow-band operation unlike TDMA-over-CDMA, SRSA can be an ideal sensor MAC protocol for applications that can tolerate relatively larger delivery latency but not frequent packet drops.

The paper presents a self-reorganizing slot allocation (SRSA) mechanism for TDMA based medium access control (MAC) in multi-cluster sensor networks. The aim is to provide a MAC layer protocol that can reduce inter-cluster TDMA interference without having to use spectrum expensive and complex wideband mechanisms such as CDMA or FDMA. The primary contribution of the paper is to demonstrate that, with adaptive slot allocation, it is possible to reduce inter-cluster interference under low loading conditions. The second contribution is to design a feedback based adaptive allocation reorganization protocol that can significantly reduce the inter-cluster interferences without relying on any global synchronization mechanisms. We present the design of SRSA and provide a simulation based characterization of the protocol in comparison with TDMA-over-CDMA, TDMA with random slot allocation and CSMA MAC protocols.

While deploying the next generation of optical networks with a mesh topology, telecommunications carriers are being confronted with a choice between wavelength switches that can switch traffic at SONET STS-48 (2.5 Gbps) granularity and subwavelength grooming capable switches that can switch at STS-1 (51 Mbps) granularity. The former consumes high fragmented/unused capacity to support low capacity end-to-end circuits using high capacity STS-48 channels (given current subwavelength traffic levels) while the latter may require relatively complicated hardware design that decreases switch scalability. Two-tier network architectures combine the benefits of STS-1 and STS-48 switches by using an upper tier of STS-48 switches for routing and restoration and a lower tier of STS-1 switches for grooming efficiency. A partial two-tier architecture, where STS-1 switches are restricted to a subset of the network nodes, has been shown in to closely match the grooming benefits of a full lower STS-1 tier. We furnish a detailed upper hound analysis of how the fragmented/unused capacity in STS-48 channels (fragmentation loss) varies with the grooming capability of a network for arbitrary traffic scenarios. We show that the upper bounds derived in this paper are in agreement with results obtained using efficient routing and grooming algorithms discussed. Because the bounds obtained do not make any assumptions about traffic and are easy to compute, they are suited for incorporation into a network engineering tool for deciding strategic placement of STS-1 switches in partial two-tier networks. Our work is not biased towards any particular network architecture but aims to analyze the grooming efficiency of two-tier networks.

In this paper we propose and analyze a new interface idling mechanism for improving energy efficiency of IEEE 802.11 based MAC hardware. A novel protocol state analysis technique is developed for detecting time windows during which a wireless interface consumes energy due to 802.11 overhearing. During this window, energy savings at the MAC layer is accomplished by forcing the wireless interface to a relatively lower-energy idling state. At the end of this window, the interface is transitioned back to its regular receiving mode. Energy savings are realized by exploiting the difference in power consumption between the overhearing state and the idling state. We evaluate the proposed protocol using ns-2 simulator. Simulation experiments validate that the proposed mechanism is capable of significantly reducing overhearing expenditure for newer 802.11 cards that support low-energy idling mode as described above. Our experimentation with Socket Communications Inc. low power 802.11 card demonstrate that the reduction in overhearing expenditure can be up to 23% and the subsequent network life extension can be up to 86% Results also show that the proposed MAC layer idling is fairly insensitive to network loading.

We study the path provisioning as a mechanism to deliver service level agreements in IP Differentiated Services networks. There is no known polynomial time solution for this problem, so we propose and analyze (by simulations) several heuristic algorithms for solving the problem. As our simulations demonstrate, a centralized server consistently delivers a better performance than a distributed solution. We also show that the performance of one of the proposed algorithms, the greedy algorithm with backtracking, can be very close to the optimal one, while being computationally feasible.

In an optical mesh network, backup channels are shared between multiple lightpaths to reduce restoration capacity overhead. The sharability of channels is usually constrained by the mandate to provide 100% recovery of all lightpaths affected by any single event failure in the network. This paper proposes a pool based channel reservation scheme that is optimal when the set of primary and backup paths (specified at link level without channel allocation) is given. In the online case, our simulations on representative network topologies show that this method improves over the existing (more restrictive) method of allocating shared backup channels using primary path diversity.

We propose a PIM-SM based IP-multicast routing framework for delivering heterogeneous quality of service to multicast receivers. We propose two tree construction algorithms: TIQM (relying on the full availability of tree-specific information about a multicast group) and NUQM (not requiring any tree specific information). While TIQM is able to compute pseudo-optimal QoS-constrained trees, it has a control-scalability problem. This problem is overcome in NUQM by restricting the amount of information used during tree computation. For realistic networks, under low to moderate network load and receivers' bandwidth requirements, performance of NUQM is close to that of TIQM. A QoS-extended intra-domain PIM-SM framework is also presented.

Since real-time variable bit rate (VBR) traffic is inherently bursty, dynamic bandwidth allocation is necessary for ATM streams that carry VBR traffic. In order to provide quality-of-services (QoS) guarantees and to reduce the computational complexity, an hybrid of guaranteed and dynamic adaptive allocation scheme requires to be implemented. Typical dynamic allocations to competing streams are done in the form of linear proportions to the bandwidth requirements. We show that during temporary link congestion such proportional arrangements can give rise to unequal queue growth and, subsequently, degraded QoS. This is found to be true even for streams that belong to the same VBR class and share identical long term traffic characteristics and QoS requirements. In this paper, four allocation algorithms are presented and analyzed in terms of their fairness and QoS potential for real-time VBR traffic. We propose and show that a novel allocation strategy, termed Minmax, solves the mentioned problem of unfairness within a class. By maintaining a fair distribution of buffer length across the streams of a class, the proposed policy can achieve better and fairer QoS performance compared to the traditional methods. We present analytical results, proofs and a simulation study of the described algorithms. Four allocation policies for handling MPEG VBR video streams are simulated in the context of a wireless ATM (WATM) medium access control. The results show that in certain scenarios, the Minmax strategy can reduce losses by an order of magnitude, while decreasing delays substantially.

In this paper, we propose a technique for reducing asynchronous transfer mode (ATM) call blocking which is achieved by splitting wide-band connections into multiple low-bandwidth subconnections and routing them independently through the network. The essence of the mechanism is to use fragmented network bandwidth for supporting calls which are otherwise blocked by conventional routing. ATM bandwidth fragmentation may take place in a situation when a connection occupies a part of a link bandwidth and the rest of it is not sufficient for another connection. The unused bandwidth becomes fragmented. We provide a detailed cell-level design for the split-scheduling algorithms, which use a special type of ATM resource management cell for maintaining cell ordering. The analysis and simulation of the scheduling algorithms show that connection splitting is capable of delivering acceptable cell-level quality of service to multiple traffic classes. We also deliver a solution for implementing splitting without requiring any protocol changes within the network. We show that it is sufficient to modify the data and control plane protocols only within the end stations. Finally, a set of routing-level simulations for splitting demonstrates that splitting can reduce blocking by up to 45% for high-bandwidth calls in a moderately loaded network. Considering that it does not incur any network expenses or protocol changes, we propose splitting as an efficient means for reducing connection blocking.

This paper presents a medium access level VBR bandwidth allocation scheme for wireless ATM (WATM) networks. After introducing a wireless ATM system model and the associated dynamic TDMA/TDD medium access control (MAC) scheme, an algorithm for VBR slot allocation is proposed. The algorithm combines information from user parameter control (UPC) at the call admission control (CAC) level with rapid MAC level estimates of the requirements for each VBR virtual circuit (VC). The method described is applicable to both conventional VBR and VBR+ in which UPC parameters are dynamically renegotiated as source bit rate requirements vary. Simulation experiments using statistically multiplexed MPEG-1 video are carried out for a typical 25 Mbps dynamic TDMA/TDD wireless ATM access link scenario. Experimental results show that, for the VBR video under consideration, the proposed scheme can achieve throughputs in the range of 60-70% while maintaining reasonable QoS.

For multirate wireless ATM traffic, the first part of the call admission process is to determine whether to admit calls as long as bandwidth is available or to deny admission to a call of a particular class even if there is enough bandwidth available, in the hope of admitting calls of some other class later. A second part of the process is to determine if the quality of service requested by the call can be met. A scenario in which the first part is particularly important is when the blocking probability requirements for different classes are different. We consider four policies to determine the circumstances under which calls of different types are admissible. For each of these policies, we show how to compute the blocking probabilities. For three of these policies, the blocking probabilities can be found by using results from product form networks. For the fourth, we provide an approximation which works extremely well in practice. We also formulate a non-linear programming problem which attempts to determine the parameters of the admissibility policies in such a way to maximize the call arrival rates while keeping the blocking probabilities under specified bounds. We provide an algorithm for solving the non-linear programming problem and use this as a basis for comparing the policies. We show that under some circumstances, it is possible to improve the system throughput by as much as 35% by a suitable use of the admissibility policies. This improvement in throughput is particularly important in wireless ATM networks, supporting high rate multimedia traffic because of the inherent limitations in bandwidth availability.

A prototype microcellular wireless asynchronous transfer mode network (WATMnet) capable of providing integrated multimedia communication services to mobile terminals is described in this paper. The experimental system's hardware consists of laptop computers (NEC Versa-M) with WATMnet interface cards, multiple VME/i960 processor-based WATMnet base stations, and a mobility-enhanced local-area ATM switch. The prototype wireless network interface cards operate at peak bit-rates up to 8 Mb/s, using low-power 2.4 GHz industrial, scientific, and medical (ISM)-band modems. Wireless network protocols at the portable terminal and base station interfaces support available bit rate (ABR), variable bit rate (VBR), and constant bit rate (CBR) transport services compatible with ATM using a dynamic time-division multiple-access/time-division duplex (TDMA/TDD) MAC protocol for channel sharing and data link control (DLC) protocol for error recovery. A custom wireless control protocol is also implemented between the portable and base units for support of radio link related functions such as user registration and handoff. All network entities including the portable, base and switch use a mobility-enhanced version of ATM ("Q.2931+") signaling for switched virtual circuit (SVC) connection control functions, including handoff. In the first stage of the prototype, the application-level API is TCP/UP over ATM ABR service class using AAL5. Early experiments with the WATMnet prototype have been conducted to validate major protocol and software aspects, including DLC, wireless control, and mobility signaling for handoff, Selected network-based multimedia/video applications requiring moderate bit-rates (/spl sim/0.5-1 Mb/s) in the ABR mode have been successfully demonstrated on the laptop PC.

A hybrid model is used to evaluate the end-to-end performance of a multihop packet radio LAN, operating under a window based flow control mechanism. The model consists of radio data-link layer, transport layer, and application layer queuing representations. The radio data-link layer is modeled by using a simulator, written for a specific CSMA-type channel access algorithm. A multichain closed queuing network for the virtual circuit based end-to-end model is solved using an approximate mean value analysis (MVA) algorithm with modified convergence schemes. Network performance measures such as end-to-end delivery delay and throughput are computed from the analysis of the hybrid model. How the network bottleneck drifts around the network with varying application service times of the terminal radio stations is studied.>

In recent years considerable interest has been shown in the development of packet-based in-building radio LANs. Most of these networks use more than one frequency together with slot reservation strategies to handle mobile traffic in a radio environment. This paper proposes an architecture and a related media access protocol, using a single frequency, to support ATM (asynchronous transfer mode) traffic in multihop wireless LANs. Fixed size packets (known as ATM cells) are used to permit statistical muitiplexing, which enables the support of multimedia traffic in radio LANs. CSMA/AED (carrier sense multiple access with acknowledgement error detection) is the protocol proposed and analysed for ATM services. In conjunction with carrier sensing, the protocol uses a per cell basis acknowledgement packet to reduce the ATM cell loss rate by indirectly detecting errors, caused either by collision or by reception of a faulty cell at the receiver. The results presented in this paper are obtained from a simulation model, developed to investigate the protocol performance at the media access layer and its effects on overall network performance.>