Breadcrumb
Developing Smart Control Platoon Algorithm for Secure VANET Environment
A vehicular ad hoc network (VANET) is a part of smart transportation. As a result of the vehicles being able to communicate with one another and share sensitive information, it is necessary to have an environment that can be trusted. Vehicles are clustered into platoons to ensure the secure transfer of information between them and select the platoon head of each platoon to control the vehicles. This paper proposes a smart control platoon system employing local and global trust schemes among vehicles in order to establish a secure environment. The platoon head calculates the local trust in each
Energy Optimization and Cost Reduction in Water Distribution Networks
Since the majority of energy consumed by water supply systems is used in transporting and distributing water, in addition to the energy required to pump the water from its sources, energy consumption is significantly associated with the water demand. Several studies have been carried out to optimize pump operations to achieve appropriate pressure and reduce the energy associated with controlling water levels in storage facilities. In this paper, we develop an optimization and decision support technique for a Water Distribution Network (WDN) that considers energy efficiency by limiting the
Sustainable Energy-Aware Task Scheduling for Wearable Medical Device Using Flower Pollination Algorithm
Power management and energy conservation are crucial for medical wearable devices that rely on energy harvesting. These devices operate under strict power budgets and require prolonged and stable operation. To achieve this, Energy-aware task scheduling is proposed as a solution to minimize energy consumption while ensuring the continued operational capabilities of the device. our paper presents a task scheduling method using the Flower Pollination Algorithm (FPA). The proposed task scheduling focuses on managing the activity of key components such as the heart rate sensor, temperature sensor
Randomization for security in half-duplex two-way Gaussian channels
This paper develops a new physical layer framework for secure two-way wireless communication in the presence of a passive eavesdropper, i.e., Eve. Our approach achieves perfect information theoretic secrecy via a novel randomized scheduling and power allocation scheme. The key idea is to allow Alice and. Bob to send symbols at random time instants. While Alice will be able to determine the symbols transmitted by Bob, Eve will suffer from ambiguity regarding the source of any particular symbol. This desirable ambiguity is enhanced, in our approach, by randomizing the transmit power level. Our
The MIMO wireless switch: Relaying can increase the multiplexing gain
This paper considers an interference network composed of K half-duplex single-antenna pairs of users who wish to establish bi-directional communication with the aid of a multiinput-multi-output (MIMO) half-duplex relay node. This channel is referred to as the "MIMO Wireless Switch" since, for the sake of simplicity, our model assumes no direct link between the two end nodes of each pair implying that all communication must go through the relay node (i.e., the MIMO switch). Assuming a delay-limited scenario, the fundamental limits in the high signal-to-noise ratio (SNR) regime is analyzed using
Smart devices for smart environments: Device-free passive detection in real environments
Device-free Passive (DfP) localization is a system envisioned to detect, track, and identify entities that do not carry any device, nor participate actively in the localization process. A DfP system allows using nominal WiFi equipment for intrusion detection, without using any extra hardware, adding smartness to any WiFi-enabled device. In this paper, we focus on the detection function of the DfP system in a real environment. We show that the performance of our previously developed algorithms for detection in a controlled environments, which achieved 100% recall and precision, degrades
Machine Learning-based Module for Monitoring LTE/WiFi Coexistence Networks Dynamics
Long-Term Evolution (LTE) technology is expected to shift some of its transmissions into the unlicensed band to overcome the spectrum scarcity problem. Nevertheless, in order to effectively use the unlicensed spectrum, several challenges have to be addressed. The most important of which is how to coexist with the incumbent unlicensed WiFi networks. Incorporating the "intelligence"component into the network radios is foreseen to resolve the intrinsic network challenges, rather than conventional non-adaptive action plans. Specifically, an intelligent cognitive engine (CE) that continuously
Generation of OFC by Self-Phase Modulation and Multiple Laser Sources in HNLF
Self-Phase Modulation (SPM) is a non-linear phenomenon relating to the self-induced phase shift encountered by the optical field during its transmission into the optical fiber. It is the most popular technique for generating an optical frequency comb (OFC) with different frequency spacing values. The SPM is regulated by many parameters such as fiber length, input optical power, and the non-linearity of the optical fiber. The OFC distinguishes between a high spectral flatness level, a high optical signal-to-noise ratio (OSNR) and a wide range of wavelengths. In this paper, The SPM uses to
Generic evaluation of FSO system over Málaga turbulence channel with MPPM and non-zero-boresight pointing errors
Free space optical (FSO) communication channels are affected by fluctuations in irradiance due to atmospheric turbulence and pointing errors. Recently, a generalized statistical model knows as Málaga (M) was developed to describe irradiance fluctuations of the beam propagating through a turbulent medium. In this paper, an approximate finite-series probability density function (PDF) for composite M turbulence with pointing errors is verified. Considering multiple pulseposition- modulation (MPPM) with intensity modulation and direct detection, specific closed-form expressions for average symbol
Early breast cancer diagnostics based on hierarchical machine learning classification for mammography images
Breast cancer constitutes a significant threat to women’s health and is considered the second leading cause of their death. Breast cancer is a result of abnormal behavior in the functionality of the normal breast cells. Therefore, breast cells tend to grow uncontrollably, forming a tumor that can be felt like a breast lump. Early diagnosis of breast cancer is proved to reduce the risks of death by providing a better chance of identifying a suitable treatment. Machine learning and artificial intelligence play a key role in healthcare systems by assisting physicians in diagnosing early, better
Pagination
- Previous page ‹‹
- Page 5
- Next page ››