Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.

/pdf/an-application-specific-protocol-architecture-for-wireless-1s7y9fiv40.pdf

An application-specific protocol architecture for wireless microsensor networks

Statistics for Spatial Data.

Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is datacentric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network.

/pdf/directed-diffusion-a-scalable-and-robust-communication-4tsx5vxsas.pdf

Directed diffusion: a scalable and robust communication paradigm for sensor networks

Mobile ad hoc routing protocols allow nodes with wireless adaptors to communicate with one another without any pre-existing network infrastructure. Existing ad hoc routing protocols, while robust to rapidly changing network topology, assume the presence of a connected path from source to destination. Given power limitations, the advent of short-range wireless networks, and the wide physical conditions over which ad hoc networks must be deployed, in some scenarios it is likely that this assumption is invalid. In this work, we develop techniques to deliver messages in the case where there is never a connected path from source to destination or when a network partition exists at the time a message is originated. To this end, we introduce Epidemic Routing, where random pair-wise exchanges of messages among mobile hosts ensure eventual message delivery. The goals of Epidemic Routing are to: i) maximize message delivery rate, ii) minimize message latency, and iii) minimize the total resources consumed in message delivery. Through an implementation in the Monarch simulator, we show that Epidemic Routing achieves eventual delivery of 100% of messages with reasonable aggregate resource consumption in a number of interesting scenarios.

/pdf/epidemic-routing-for-partially-connected-ad-hoc-networks-s5rslcaiip.pdf

Epidemic routing for partially-connected ad hoc networks

http://www.csun.edu/~andrzej/COMP529-S05/papers/Mesh%20Networks%20Survey.pdf

Wireless mesh networks: a survey

Given the exponential increase in broadband cellular traffic it is imperative that scalable traffic measurement and monitoring techniques be developed to aid various resource management methods. In this paper, we use a machine learning technique to learn the underlying conditional dependence and independence structure in the base station traffic loads to show how such probabilistic models can be used to reduce the traffic monitoring efforts. The broad goal is to exploit the model to develop a spatial sampling technique that estimates the loads on all the base stations based on actual measurements only on a small subset of base stations. We take special care to develop a sparse model that focuses on capturing only key dependences. Using trace data collected in a network of 400 base stations we show the effectiveness of this approach in reducing the monitoring effort. To understand the tradeoff between the accuracy and monitoring complexity better, we also study the use of this modeling approach on real applications. Two applications are studied — energy saving and opportunistic scheduling. They show that load estimation via such modeling is quite effective in reducing the monitoring burden.

Learning probabilistic models of cellular network traffic with applications to resource management

Impact of caching and MAC overheads on routing performance in ad hoc networks

For bandwidth-efficient streaming of 360-degree videos, the streaming technique must adapt both to the changing viewport of the user and variations of the available network bandwidth. The state-of-the-art streaming techniques for this problem attempt to solve an optimization using simplified rules that do not adapt very well to the uncertainties related to the viewport or network. We adopt a 3D-Convolutional Neural Networks (3DCNN) model to extract spatio-temporal features of videos and predict the viewport. Given the sequential decision-making nature of such streaming technique, we then apply a Reinforcement Learning (RL) based adaptive streaming approach. We address the challenges of using RL in this scenario, such as large action space and delayed reward evaluation. Comprehensive evaluations with real network traces show that the proposed method outperforms three tile-based streaming techniques for 360-degree videos. Compared to the tile-based streaming techniques, the average user-perceived bitrate of the proposed method is 1.3-1.7 times higher and the average quality of experience of the proposed method is also 1.6-3.4 times higher. Subjective user studies further confirm the superiority of the proposed approach.

/pdf/adaptive-streaming-of-360-degree-videos-with-reinforcement-498mbhpqqa.pdf

Adaptive Streaming of 360-Degree Videos with Reinforcement Learning

With increase in cellular-enabled IoT devices having diverse traffic characteristics and service level objectives (SLOs), handling the control traffic in a scalable and resource-efficient manner in the cellular packet core network is critical. The traditional monolithic design of the cellular core adopted by service-providers is inflexible with respect to the diverse requirements and bursty loads of IoT devices, specifically for properties such as elasticity, customizability, and scalability. To address this key challenge, we focus on the most critical control plane component of the cellular packet core network, the Mobility Management Entity (MME). We present MMLite, a functionally decomposed and stateless MME design wherein individual control procedures are implemented as microservices and states are decoupled from their processing, thus enabling elasticity and fault tolerance. For SLO compliance, we develop a multi-level load balancing approach based on skewed consistent hashing to efficiently distribute incoming connections. We evaluate the performance benefits of MMLite over existing approaches with respect to scaling, fault tolerance, SLO compliance and resource efficiency.

https://dl.acm.org/doi/pdf/10.1145/3314148.3314345

MMLite: A Scalable and Resource Efficient Control Plane for Next Generation Cellular Packet Core

With the advent of VLSI it has become possible to map parallel algorithms for compute-bound problems directly on silicon. Systolic architecture is very good candidate for VLSI implementation because of its regular and simple design, and regular communication pattern. In this paper, a systolic algorithm and corresponding systolic architecture, a linear systolic array, for the scanline-based hidden surface removal problem in three-dimensional computer graphics have been proposed. The algorithm is based on the concept of sample spans or intervals. The worst case time taken by the algorithm is O(n), n being the number of segments in a scanline. The time taken by the algorithm for a given scene depends on the scene itself, and on an average considerable improvement over the worst case behaviour is expected. A pipeline scheme for handling the I/O process has also been proposed which is suitable for VLSI implementation of the algorithm.

Samir R. Das

Papers

Learning probabilistic models of cellular network traffic with applications to resource management

Impact of caching and MAC overheads on routing performance in ad hoc networks

Adaptive Streaming of 360-Degree Videos with Reinforcement Learning

MMLite: A Scalable and Resource Efficient Control Plane for Next Generation Cellular Packet Core

A systolic algorithm for hidden surface removal