Bio: Srijan Chakraborty is an academic researcher from Purdue University. The author has contributed to research in topics: Wireless network & Heuristics. The author has an hindex of 1, co-authored 1 publications receiving 38 citations.
10 Jun 2003
TL;DR: The results show that, with relatively simple and, hence, efficient prediction heuristics, energy savings in communication can significantly outweigh the energy expenses in executing the prediction algorithms.
Abstract: Node movement can be exploited to reduce the energy consumption of wireless network communication The strategy consists in delaying communication until a mobile node moves close to its target peer node, within an application-imposed deadline We evaluate the performance of various heuristics that, based on the movement history of the mobile node, estimate an optimal time (in the sense of least energy use) of communication subject to the delay constraint We evaluate the impact of node movement model, length of movement history maintained, allowable delay, single hop versus multiple hop communication, and size of data transfer on the energy consumption We also present measurement results on an iPAQ pocket PC that quantify energy consumption in executing the prediction algorithms Our results show that, with relatively simple and hence efficient prediction heuristics, energy savings in communication can significantly outweigh the energy expenses in executing the prediction algorithms
11 Jun 2022
TL;DR: This paper proposes SpEaC --- a coarse-grained reconfigurable spatial architecture - as an energy-efficient programmable processor for earable applications, which outperforms programmable cores modeled after M4, M7, A53, and HiFi4 DSP by 99.3% and outperforms low power Mali T628 MP6 GPU across all kernels.
Abstract: Earables such as earphones [15, 16, 73], hearing aids , and smart glasses [2, 14] are poised to be a prominent programmable computing platform in the future. In this paper, we ask the question: what kind of programmable hardware would be needed to support earable computing in future? To understand hardware requirements, we propose EarBench, a suite of representative emerging earable applications with diverse sensor-based inputs and computation requirements. Our analysis of EarBench applications shows that, on average, there is a 13.54×-3.97× performance gap between the computational needs of EarBench applications and the performance of the microprocessors that several of today's programmable earable SoCs are based on; more complex microprocessors have unacceptable energy efficiency for Earable applications. Our analysis also shows that EarBench applications are dominated by a small number of digital signal processing (DSP) and machine learning (ML)-based kernels that have significant computational similarity. We propose SpEaC --- a coarse-grained reconfigurable spatial architecture - as an energy-efficient programmable processor for earable applications. SpEaC targets earable applications efficiently using a) a reconfigurable fixed-point multiply-and-add augmented reduction tree-based substrate with support for vectorized complex operations that is optimized for the earable ML and DSP kernel code and b) a tightly coupled control core for executing other code (including non-matrix computation, or non-multiply or add operations in the earable DSP kernel code). Unlike other CGRAs that typically target general-purpose computations, SpEaC substrate is optimized for energy-efficient execution of the earable kernels at the expense of generality. Across all our kernels, SpEaC outperforms programmable cores modeled after M4, M7, A53, and HiFi4 DSP by 99.3×, 32.5×, 14.8×, and 9.8× respectively. At 63 mW in 28 nm, the energy efficiency benefits are 1.55 ×, 9.04×, 68.3 ×, and 32.7 × respectively; energy efficiency benefits are 15.7 × -- 1087 × over a low power Mali T628 MP6 GPU.
••24 May 2004
TL;DR: This work presents the first mobility control scheme for improving communication performance in large-scale networks of mobile agents autonomously performing long-term sensing and communication tasks and provides extensive evaluations on the feasibility of mobility control, showing that controlled mobility can improve network performance in many scenarios.
Abstract: In the near future, the advent of large-scale networks of mobile agents autonomously performing long-term sensing and communication tasks will be upon us. However, using controlled node mobility to improve communication performance is a capability that the mobile networking community has not yet investigated. In this paper, we study mobility as a network control primitive. More specifically, we present the first mobility control scheme for improving communication performance in such networks. Our scheme is completely distributed, requiring each node to possess only local information. Our scheme is self-adaptive, being able to transparently encompass several modes of operation, each respectively improving power efficiency for one unicast flow, multiple unicast flows, and many-to-one concast flows. We provide extensive evaluations on the feasibility of mobility control, showing that controlled mobility can improve network performance in many scenarios. This work constitutes a novel application of distributed control to networking in which underlying network communication serves as input to local control rules that guide the system toward a global objective.
••13 Jun 2007
TL;DR: Simulations based on field-collected traces show that algorithms can improve the average battery lifetime of a commercial mobile phone for a three-channel electrocardiogram (ECG) reporting application by 39%, very close to the theoretical upper bound of 42%.
Abstract: Ubiquitous connectivity on mobile devices will enable numerous new applications in healthcare and multimedia. We set out to check how close we are towards ubiquitous connectivity in our daily life. The findings from our recent field-collected data from an urban university population show that while network availability is decent, the energy cost of network interfaces poses a great challenge. Based on our findings, we propose to leverage the complementary strength of Wi-Fi and cellular networks by choosing wireless interfaces for data transfers based on network condition estimation. We show that an ideal selection policy can more than double the battery lifetime of a commercial mobile phone, and the improvement varies with data transfer patterns and Wi-Fi availability.We formulate the selection of wireless interfaces as a statistical decision problem. The key to attaining the potential battery improvement is to accurately estimate Wi-Fi network conditions without powering up its network interface. We explore the use of different context information, including time, history, cellular network conditions, and device motion, for this purpose. We consequently devise algorithms that can effectively learn from context information and estimate the probability distribution of Wi-Fi network conditions. Simulations based on field-collected traces show that our algorithms can improve the average battery lifetime of a commercial mobile phone for a three-channel electrocardiogram (ECG) reporting application by 39%, very close to the theoretical upper bound of 42%. Finally, our field validation of our most simple algorithm demonstrates a 35% improvement in battery lifetime.
••06 Jun 2005
TL;DR: Practical, power-aware, general-purpose algorithms for component placement and migration are evaluated and it is demonstrated that they can significantly increase system longevity by effectively distributing energy consumption and avoiding hotspots.
Abstract: In this paper, we describe the design and implementation of a distributed operating system for ad hoc networks. Our system simplifies the programming of ad hoc networks and extends total system lifetime by making the entire network appear as a single virtual machine. It automatically and transparently partitions applications into components and dynamically finds them a placement on nodes within the network to reduce energy consumption and to increase system longevity. This paper describes our programming model, outlines the design and implementation of our system and examines the energy efficiency of our approach through extensive simulations as well as validation of a deployment on a physical testbed. We evaluate practical, power-aware, general-purpose algorithms for component placement and migration, and demonstrate that they can significantly increase system longevity by effectively distributing energy consumption and avoiding hotspots.
TL;DR: This paper presents SeeMon, a scalable and energy-efficient context monitoring framework for sensor-rich, resource-limited mobile environments, and implements and test a prototype system, which achieves a high level of scalability and energy efficiency.
Abstract: The key feature of many emerging pervasive computing applications is to proactively provide services to mobile individuals. One major challenge in providing users with proactive services lies in continuously monitoring users' context based on numerous sensors in their PAN/BAN environments. The context monitoring in such environments imposes heavy workloads on mobile devices and sensor nodes with limited computing and battery power. We present SeeMon, a scalable and energy-efficient context monitoring framework for sensor-rich, resource-limited mobile environments. Running on a personal mobile device, SeeMon effectively performs context monitoring involving numerous sensors and applications. On top of SeeMon, multiple applications on the mobile device can proactively understand users' contexts and react appropriately. This paper proposes a novel context monitoring approach that provides efficient processing and sensor control mechanisms. We implement and test a prototype system on two mobile devices: a UMPC and a wearable device with a diverse set of sensors. Example applications are also developed based on the implemented system. Experimental results show that SeeMon achieves a high level of scalability and energy efficiency.
TL;DR: iMobif, a flow-based informed mobility framework that collects network information for mobility decision making is proposed, and it is demonstrated how to use iMobif to minimize total communication energy consumption as well as to maximize system lifetime.
Abstract: Energy optimization is important in wireless ad hoc networks, where node battery power is usually limited. Research results show that such a network can exploit controlled node mobility to reduce communication-related energy consumption. However, node movement itself usually consumes energy. In this paper we study the energy optimization problem that accounts for energy costs associated with both communication and physical node movement. We refer to this model as informed mobility. We first review the theoretical foundations on how to reduce total communication energy consumption, as well as increase system lifetime, by combining node movement and transmission power adaptation. Next, we describe and analyze the informed mobility optimization problem. Based on this analysis, we introduce localized algorithms and protocols for informed mobility. We propose iMobif, a flow-based informed mobility framework that collects network information for mobility decision making. We demonstrate how to use iMobif to minimize total communication energy consumption as well as to maximize system lifetime. We compare the performance of iMobif to that of systems with no mobility or only cost-unaware mobility. Simulation results show iMobif is effective in reducing energy consumption relative to such systems