Search arXivSearch

arXiv · 1609.02603

Cutting down energy usage in wireless sensor networks using Duty Cycle technique and multi-hop routing

Abstract

A wireless sensor network is composed of many sensor nodes that have been given out in a specific zone and each of them had an ability of collecting information from the environment and sending collected data to the sink. The most significant issues in wireless sensor networks despite the recent progress is the trouble of the severe limitations of energy resources. Since that in different applications of sensor nets we could throw a static or mobile sink then all aspects of such networks should be planned with an awareness of energy. One of the most significant topics related to these networks is routing. One of the most widely used and efficient methods of routing is a hierarchy (based on clustering) method. In The present study with the objective of cutting down energy consumption and persistence of network coverage we have offered a novel algorithm based on clustering algorithms and multi hop routing. To achieve this goal first we layer the network environment based on the size of the network. We will identify the optimal number of cluster heads and every cluster head based on the mechanism of topology control will start to accept members. Likewise we set the first layer as gate layer and subsequently identifying the gate nodes we would turn away half of the sensors and then stop using energy and the remaining nodes in this layer will join the gate nodes because they hold a critical part in bettering the functioning of the system. Cluster heads off following layers send the information to cluster heads in the above layer until sent data will be sent to gate nodes and finally will be sent to sink. We have tested the proposed algorithm in two situations 1) when the sink is off and 2) when a sink is on and simulation data shows that proposed algorithm has better performance in terms of the life span of a network than LEACH and E LEACH protocols.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ali Sedighimanesh, Mohammad Sedighimanesh, Javad Baqeri. 2016-09-08. Cutting down energy usage in wireless sensor networks using Duty Cycle technique and multi-hop routing. https://doi.org/10.5121/ijwmn.2016.8402

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

CSI Simulation: Why Additive Noise Fails and How to Fix It

Channel State Information (CSI) has become a widely used wireless channel sensing modality for applications such as indoor localization, activity recognition, and respiration monitoring. Because collecting labeled data under every target condition is impractical, training CSI-based models often relies on simulated data produced by adding noise or perturbations to recorded channel estimates, most commonly additive white Gaussian noise (AWGN). This practice assumes that the receiver chain between the antenna and the channel estimator is linear and gain-invariant. We test this assumption empirically using RF jamming as a controlled perturbation on 6 commodity receivers across 2 indoor environments. The assumption does not hold. Automatic gain control compresses the channel estimate multiplicatively before digitization, producing amplitude distributions that no additive noise variance can reproduce. To close the resulting fidelity gap, we propose M_QTC, a measurement-calibrated model that learns the per-subcarrier distribution transformation through quantile mapping, temporal filtering, and copula-based cross-subcarrier reordering. M_QTC reduces amplitude error 8-fold and closes 89% of the aggregate fidelity gap across four complementary dimensions. The improvement transfers directly to downstream tasks, where 5 classifiers from different families trained on M_QTC-simulated data recover 93% of real-data jamming detection performance, while AWGN-trained classifiers remain near random decision.

cs.NI

BALANCE: Hybrid Autoregressive-Speculative LLM Inference at the Network Edge

Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference mainly relies on two approaches: Autoregressive decoding (AD) generates output tokens sequentially, resulting in long latency; Speculative decoding (SD) accelerates inference by using a small language model (SLM) to generate multiple draft tokens for LLM verification, but incurs extra memory costs. Due to this latency-memory tradeoff, neither approach alone can efficiently serve users with heterogeneous demands under limited edge computing resources. To address this challenge, we propose a hybrid autoregressive-speculative inference (BALANCE) framework for edge LLM inference. In BALANCE, an edge server hosts both an SLM and an LLM, admits users, assigns each admitted user to the AD or SD mode, and performs the two modes simultaneously. To maximize the number of served users, we formulate a task throughput maximization problem to jointly determine user admission and computing resource allocation between AD and SD under user latency requirements and server memory constraints. Since the problem is NP-hard, we develop a polynomial-time algorithm that transforms the original problem into two sub-problems and obtains a sub-optimal solution with a constant approximation guarantee. Experiments demonstrate that BALANCE consistently outperforms conventional AD and SD and significantly improves task throughput.

cs.NI

Zero-Knowledge Remote Adversarial Attack against Wi-Fi-based Human Activity Recognition for Privacy Protection

The growing capability of Wi-Fi devices to identify human activities using channel state information (CSI) raises privacy concerns. To counter this threat, we propose GRAW, an adversary system, acting as a privacy defender, that degrades the human activity recognition (HAR) system at the user device by perturbing the router's signals that the device uses to estimate CSI. GRAW employs generative adversarial imitation learning (GAIL) to construct perturbation signals, and thereby eliminates the need for any information on the target HAR systems and their inputs (i.e., zero-knowledge operation). We evaluate GRAW against seven representative HAR models, using datasets collected in five environments, including our own dataset. We observe that GRAW is the only remote attack scheme that degrades every tested HAR model to a random-selection level. At the same perturbation level, GRAW achieves an attack success ratio up to 76.7% higher than comparison methods, while maintaining over 99% packet success rate on regular Wi-Fi communication. We demonstrate the feasibility of GRAW through real-time, over-the-air experiments with software-defined radios.

cs.NI