Task-Oriented Mulsemedia Communication using Unified Perceiver and Conformal Prediction in 6G Metaverse Systems

The growing prominence of extended reality (XR), holographic-type communications, and metaverse demands truly immersive user experiences by using many sensory modalities, including sight, hearing, touch, smell, taste, etc. Additionally, the widespread deployment of sensors in areas such as agriculture, manufacturing, and smart homes is generating a diverse array of sensory data. A new media format known as multisensory media (mulsemedia) has emerged, which incorporates a wide range of sensory modalities beyond the traditional visual and auditory media. 6G wireless systems are envisioned to support the internet of senses, making it crucial to explore effective data fusion and communication strategies for mulsemedia. In this paper, we introduce a task-oriented multi-task mulsemedia communication system named MuSeCo, which is developed using unified Perceiver models and Conformal Prediction. This unified model can accept any sensory input and efficiently extract latent semantic features, making it adaptable for deployment across various Artificial Intelligence of Things (AIoT) devices. Conformal Prediction is employed for modality selection and combination, enhancing task accuracy while minimizing data communication overhead. The model has been trained using six sensory modalities across four classification tasks. Simulations and experiments demonstrate that MuSeCo can effectively select and combine sensory modalities, significantly reduce end-to-end communication latency and energy consumption, and maintain high accuracy in communication-constrained systems

Error Control in Wireless Sensor Networks: A Cross Layer Analysis

Error control is of significant importance for Wireless Sensor Networks (WSNs) because of their severe energy constraints and the low power communication requirements. In this paper, a cross-layer methodology for the analysis of error control schemes in WSNs is presented such that the effects of multi-hop routing and the broadcast nature of the wireless channel are investigated. More specifically, the cross-layer effects of routing, medium access, and physical layers are considered. This analysis enables a comprehensive comparison of forward error correction (FEC) codes, automatic repeat request (ARQ), and hybrid ARQ schemes in WSNs. The validation results show that the developed framework closely follows simulation results. Hybrid ARQ and FEC schemes improve the error resiliency of communication compared to ARQ. In a multi-hop network, this improvement can be exploited by constructing longer hops (hop length extension), which can be achieved through channel-aware routing protocols, or by reducing the transmit power (transmit power control). The results of our analysis reveal that for hybrid ARQ schemes and certain FEC codes, the hop length extension decreases both the energy consumption and the end-to-end latency subject to a target packet error rate (PER) compared to ARQ. This decrease in end-to-end latency is crucial for delay sensitive, real-time applications, where both hybrid ARQ and FEC codes are strong candidates. We also show that the advantages of FEC codes are even more pronounced as the network density increases. On the other hand, transmit power control results in significant savings in energy consumption at the cost of increased latency for certain FEC codes. The results of our analysis also indicate the cases where ARQ outperforms FEC codes for various end-to-end distance and target PER values

XLP: A Cross-Layer Protocol for Efficient Communication in Wireless Sensor Networks

Severe energy constraints of battery-powered sensor nodes necessitate energy-efficient communication in Wireless Sensor Networks (WSNs). However, the vast majority of the existing solutions is based on classical layered protocols approach, which leads to significant overhead. It is much more efficient to have a unified scheme which blends common protocol layer functionalities into a cross-layer module. In this paper, a cross layer protocol (XLP) is introduced, which achieves congestion control, routing, and medium access control in a cross-layer fashion. The design principle of XLP is based on the cross-layer concept of initiative determination, which enables receiver-based contention, initiative-based forwarding, local congestion control, and distributed duty cycle operation to realize efficient and reliable communication in WSNs. The initiative determination requires simple comparisons against thresholds, and thus is very simple to implement, even on computationally impaired devices. To the best of our knowledge, XLP is the first protocol that integrates functionalities of all layers from PHY to transport into a cross-layer protocol. A cross-layer analytical framework is developed to investigate the performance of the XLP. Moreover, in a cross-layer simulation platform, the state-of-the- art layered and cross-layer protocols have been implemented along with XLP for performance evaluations. XLP significantly improves the communication performance and outperforms the traditional layered protocol architectures in terms of both network performance and implementation complexity

XLP: A Cross-Layer Protocol for Efficient Communication in Wireless Sensor Networks

Severe energy constraints of battery-powered sensor nodes necessitate energy-efficient communication in Wireless Sensor Networks (WSNs). However, the vast majority of the existing solutions is based on classical layered protocols approach, which leads to significant overhead. It is much more efficient to have a unified scheme which blends common protocol layer functionalities into a cross-layer module. In this paper, a cross layer protocol (XLP) is introduced, which achieves congestion control, routing, and medium access control in a cross-layer fashion. The design principle of XLP is based on the cross-layer concept of initiative determination, which enables receiver-based contention, initiative-based forwarding, local congestion control, and distributed duty cycle operation to realize efficient and reliable communication in WSNs. The initiative determination requires simple comparisons against thresholds, and thus is very simple to implement, even on computationally impaired devices. To the best of our knowledge, XLP is the first protocol that integrates functionalities of all layers from PHY to transport into a cross-layer protocol. A cross-layer analytical framework is developed to investigate the performance of the XLP. Moreover, in a cross-layer simulation platform, the state-of-the- art layered and cross-layer protocols have been implemented along with XLP for performance evaluations. XLP significantly improves the communication performance and outperforms the traditional layered protocol architectures in terms of both network performance and implementation complexity

Joint energy harvesting and communication analysis for perpetual wireless nanosensor networks in the terahertz band

Abstract—Wireless nanosensor networks (WNSNs) consist of nanosized communicating devices, which can detect and measure new types of events at the nanoscale. WNSNs are the enabling technology for unique applications such as intrabody drug delivery systems or surveillance networks for chemical attack prevention. One of the major bottlenecks in WNSNs is posed by the very limited energy that can be stored in a nanosensor mote in contrast to the energy that is required by the device to communicate. Recently, novel energy harvesting mechanisms have been proposed to replenish the energy stored in nanodevices. With these mechanisms, WNSNs can overcome their energy bottleneck and even have infinite lifetime (perpetual WNSNs), provided that the energy harvesting and consumption processes are jointly designed. In this paper, an energy model for self-powered nanosensor motes is developed, which successfully captures the correlation between the energy harvestin