The increasing prevalence of smartphones has made pedestrian safety on roads a significant concern due to distractions caused by these devices. This paper introduces a unique and real-time assistance system, UWB-assisted Safe Walk (UASW), designed to detect obstacles and terrain features and warn users about ongoing situations. Our proposed technique harnesses the capabilities of Impulse Radio Ultra-Wideband (IR-UWB) radar integrated within the smartphone, which delivers exceptional range resolution and strong noise immunity employing short pulses. We developed UASW specifically for Samsung smartphones featuring IR-UWB connectivity with machine/deep learning (ML/DL) techniques. Our framework utilizes complex Channel Impulse Response (CIR) data to combine signal processing-based detection with artificial neural network (ANN)-driven classification for obstacles and terrains. The performance of our suggested UASW system is assessed using real-world collected data. Experimental outcomes indicate that the proposed UASW system achieves an obstacle detection accuracy rate of up to 97 % and obstacle and terrain classification accuracy of approximately 95%. With an inference latency of around 26 milliseconds, UASW effectively supports smartphone-distracted pedestrians and enhances their situational awareness.

In this paper, we propose SurroundSense, a novel event-driven framework that generates contextual information based on the user's surroundings using the Impulse Radio Ultra-Wideband (IR-UWB) Radar integrated into smartphones. By leveraging the channel impulse response (CIR) data obtained from the IR-UWB radar, SurroundSense constructs a detailed contextual understanding of the user's environment, allowing for tailored recommendations based on the surrounding conditions. The system processes the CIR data and combines it with acoustic sensing information, specifically noise levels and ambient light information to generate the surrounding information. Further-more, utilizing the IMU sensors enables an expanded field-of-view (FOV), providing a comprehensive$360^o$environmental awareness. This enhanced surrounding information enhances the accuracy and pertinence of personalized recommendations. Our approach showcases the potential of incorporating IR-UWB technology into personalization algorithms, ultimately improving user experiences and facilitating adaptive, context-aware applications.

Rapid advancement of radar technology has led to the emergence of cognitive radar systems, which utilize adaptive mechanisms to optimize performance in dynamic environments. This paper explores the integration of cognitive adaptability into smartphone-based Impulse Radio Ultra-Wideband (IRUWB) radar systems. By dynamically modifying the radar’s operational parameters based on real-time output analysis, we aim to address the limitations of current smartphone radar implementations, including high power consumption, static radar configurations, and the inherent mobility of smartphones. Our proposed Cognitive-Adaptive IR-UWB Radar (CAIR) system improves accuracy, power efficiency, and responsiveness, enabling effective target detection, target classification, gesture recognition, distance estimation, and vital sign monitoring in diverse scenarios. By incorporating cognitive radar principles, we present a novel approach to overcoming the challenges of varying environmental conditions and user contexts, ultimately delivering a more robust and versatile user experience. This paper outlines the CAIR architecture, algorithmic design, and adaptive control mechanisms, showcasing its potential to enhance smartphone radar sensing. When tested against the major smartphone use cases, our system improves accuracy by up to 11.5%, while achieving cognitive adaptability of up to 90%. Additionally, the Artificial Neural Network (ANN)-based cognitive model achieves an accuracy of 95% and an F1-score of 94%.

The next-generation networks (Beyond 5G and 6G) are witnessing a tremendous increase in Control Plane (CP) signaling due to Service Based Architecture. The 5G Core deploys a Cloud Native Load Balancer (CNLB) to load balance CP signals between Cloud-native Network Functions (CNFs). Typically, 5G network slices like Ultra Reliable Low Latency Communications (URLLC) share the same CP resources across other slices. However, existing CNLB fails to balance CP resources between slice flows. Further, CNLB deploys simple scheduling (Weighted Round Robin and Least Connection) for load balancing and cannot address diverse needs of URLLC services like latency and reliability. We propose an extensible Harmonizer Framework that harmonizes diverse needs of different slice flows by leveraging AI/ML techniques. This enhances CNLB to prioritize and load balance CP signaling efficiently. Our simulation results show around 5-15 ms decrease in CP path setup time and 0.001-0.027% improved reliability of URLLC flows between User Equipments and CNFs.

Operating in high-frequency bands such as mmWave and Terahertz poses challenges due to frequent variations in channel quality. These fluctuations impact the radio protocol stack, increasing latency and reducing throughput. Existing transport layer protocols need help to adapt to the high variability of link quality and network capacity, leading to the under-utilization of resources. The absence of radio link information further hinders the transport layer's ability to handle dynamic channel conditions. This paper presents Machine Learning-based Cross Layer Improvement (ML-CLI) of the transport layer, a novel solution designed to address the challenges posed by dynamic link variations in high-frequency bands. ML-CLI leverages real-time wireless network quality estimation to optimize the transport layer for an enhanced quality of service (QoS). Various ML and deep learning models for link quality prediction are evaluated, with the Artificial Neural Network (ANN) model emerging as the top-performing model, achieving an accuracy of 98.1% and an F1-score of 0.98. The integration of ML-CLI into the ns3 simulator enables the assessment of its impact on the transport layer. The results demonstrate substantial goodput and packet loss ratio improvements, with ML-CLI providing faster recovery and improved congestion control. Notably, ML-CLI achieves a 45.22% improvement in goodput and up to a 38.52% reduction in packet loss ratio compared to the traditional TCP Cubic variant.

As communication systems move towards 5G and beyond, utilizing a higher carrier frequency is essential to accommodate the exponential increment of data traffic. However, the short-range characteristics of high-frequency bands, such as mmWave and TeraHertz will cause frequent channel quality fluctuations. Also, radio protocol stack events triggered by these channel variations cause increased latency and reduced throughput. The existing transport protocols do not adapt well to such high variability of link quality and irregular network capacity, leading to the under-utilization of the network resources. More-over, the lack of radio link information hampers the transport layer in coping with dynamic channel conditions. The proposed Link Quality-assisted Transport (LQaT) solution estimates the link quality in real-time and notifies the end-host for adapting the transport layer to the varying wireless conditions. LQaT improves congestion/flow control to minimize the impact of link fluctuation and enhance the user experience. The experiments in ns-3 show that LQaT improves throughput up to 34% and reduces latency by up to 28% consistently, with effective link-layer adaptations.

Edge computing in 5G and Beyond 5G (B5G) networks is an essential infrastructure enabler for future technology and business developments, also known as Industry 4.0, which requires high computational power and fast service deliveries. There are many proprietary Multi-access Edge Computing (MEC) architectures defined by the service providers and the standard bodies. However, all of them require changes not only in the existing end devices, but also in the existing applications to connect with the MEC platform for edge server discovery. Furthermore, these client applications are unaware of the list of server applications running on the edge servers. It also requires installation of device application in mobile devices to subscribe and authorize with the MEC Platform, to communicate with edge servers and to find the available MEC services, which cause additional overhead to the device. In this paper, we propose a novel solution named Computational and Location-Aware Middleware (CLM), to compute and locate the nearest server for service discovery and computational offload for the mobile device. The proposed solution does not require any changes in existing applications for edge server discovery, and it has been successfully prototyped in Samsung devices with Android Pie for evaluation. The results show a reduction in edge server discovery overheads to one Round Trip Time and remarkable performance gain of up to 85% for service discovery.

The Fifth Generation (5G) network provides a platform for emerging technologies such as vehicular communication, massive IoT, and tactile internet. It is paving the way to a modern autonomous industry with mobility as a key requirement to achieve seamless end-to-end connectivity. But the current transport layer is not well equipped with the evolving lower layers to deal with the dynamics of the network conditions during mobility. The variation in network conditions and frequent disturbance in lower layers due to handovers causes performance degradation in the transport layer. Hence, we propose a novel solution called Mobility aware Socket Layer (MaSL), an end-to-end solution for futuristic applications. It is a software solution that provides seamless connectivity to the end devices during user mobility and frequent handover scenarios. It communicates with the lower layers and effectively controls the transport layer for achieving improved Quality-of-Service (QoS) with enhanced user experience. We prototyped MaSL in mobile devices and evaluated the performance using the ns-3 simulator as well as live-air experiments. The experiments are conducted using Wi-Fi and 5G networks, and the results show a significant reduction in reconnection latencies around 33% and improved end-user throughput by up to 16%.

The Next Generation Networks (NGN) set its standard to provide very high data rates, Ultra-Reliable Low Latency Communications (URLLC), increased network capacity and significantly improved Quality of Service (QoS). Thus, it provides an infrastructure for the Internet of Things (IoT) to power billions of connected devices. With this upsurge in IoT devices, the need for adopting IPv6 over exhausting IPv4 addresses becomes more unavoidable. Hence, Internet Service Providers (ISP's) are adopting IPv6 along with IPv4 addresses using an address transition method called dual-stack. However, the Dual-Stack network causes relatively more connectivity overhead than the single stack network. For example, DNS lookup and TCP connection time are comparatively high in dual-stack mobile devices. This affects the page loading time of the application, thereby impacting the user experience significantly. In this paper, we analyzed all these connectivity overheads and propose a novel solution called NexGen Connectivity optimizer (NexGenCO), which provides better connectivity for the applications using network-aware concurrency and intelligent DNS caching. NexGenCO is prototyped and evaluated in Samsung devices with Android Pie. It significantly reduces connectivity overhead and improves page loading time up to 18% consistently.

At present, most of the research work going around focuses on evolution of Next Generation Networks (NGN). The primary focus of this research work is towards improving the performance by reducing latency in network, increasing peak throughput and improving spectral efficiencies. Even though Fifth Generation (5G) network standards set its requirement to lower the latency, the Internet Protocol (IP) suite introduces significant delay in the network. For example, Domain Name System (DNS) resolution in a device takes at least one Round Trip Time (RTT) irrespective of the network infrastructure. Sometimes, slow responsiveness of DNS server triggers client to send multiple queries, which results in user perceived delay in the client application. Moreover, DNS resolution delays socket set-up time and creates connectivity overhead to the client application and also affects user experience significantly. We considered this limitation and propose a novel solution called Flare-DNS Resolver (FDR). It is a lightweight and client only solution which can be easily deployed across all mobile platforms. We successfully implemented FDR in Samsung flagship models having Android Oreo Operating System. The recent version of FDR is tested in both Samsung Galaxy S8 and S9 variants. FDR significantly improves application page loading time by 10% to 15% consistently.

As the daily usage time of smart phone increases, battery life time becomes increasingly important. Recently, a complaint is raised for frequent battery drain in smart phones due to applications running in the background. In most of the mobile applications, after end of the data transfer, longer idle time occurs in a Transmission Control Protocol (TCP) connection for connection closure. Usually such idle TCP connections are terminated by servers based on a static timeout value configured at the server side. This results in frequent transition from idle Radio Frequency (RF) state to active RF state thereby increasing mobile network signalling and device battery power consumption. To address this problem, we propose a novel TCP connection control mechanism in view of power saving, called Low Power TCP (LPTCP). Our solution is lightweight, application agnostic, a client deployed solution. LPTCP identifies inactive TCP connections that cause the delayed server close and pro-actively closes those TCP connections. We evaluated LPTCP with the latest Samsung smart phones such as Galaxy S8 and S9. LPTCP achieved up to 18% of power gain and significantly reduced 24% of mobile network signalling overhead.

The primary focus of Next Generation Networks (NGN) is towards improving the performance by reducing latency in the network, increasing peak throughput and improving spectral efficiencies. Even though Fifth Generation (5G) network set its standard to lower the latency, the existing TCP/IP protocol suite imparts significant overhead to Next Generation Transport Layer. For example, the web contents are hosted across multiple content servers redundantly, which can be accessed using different network interfaces available in the client device. These mirror servers connected with different network interfaces will have different network path quality. The network path quality for any server changes based on user mobility, type of network interface (Wi-Fi/Cellular) and congestion in the path. In general, this network path quality impacts the RTT of the network which affects the content downloading time for all applications. At any situation, the client application does not know the best network path available at the moment. Hence, we propose a novel solution called Redundant TCP Connector (RTC) which establishes simultaneous connection using multiple network interfaces available and dynamically connects to the best estimated network path at any moment. RTC is a lightweight client only software solution which is prototyped in Samsung flagship models with Android O. RTC significantly improves content downloading time by 10% to 15% consistently.

Applications (Apps) in Smart devices make our life connected to Internet all the time. Many of the Apps like Google Apps, Facebook, and Twitter generate periodic background traffic. Due to this background traffic, battery continuously drains in Smart Phones. In this paper, we identify root causes for the background traffic and evaluate Apps' behavior with respect to impacts of TCP connection flow on Smart phone battery. We have conducted extensive experiments and collected data from various operator networks in different countries. We observe that TCP delayed closures not only extend radio wake up time but also lead to prolonged retransmissions. These prolonged retransmissions lead to severe battery drain problem. We propose a solution for such TCP closures at the client side with the objective of controlling unwanted TCP retransmissions. Our proposed solution is energy efficient, light weight, application agnostic, and a client only solution which makes deployment easier. Our solution has been evaluated for Android devices such as Samsung Galaxy S6, S7 variants, J7, and the Tizen device such as Samsung Z3. We have found that our solution consistently achieves 10 to 20 percent of power usage reduction irrespective of varying network and traffic conditions. We provide detailed experimental results considering various corner cases and also present the implementation architecture of our proposed solution.

Applications (Apps) in Smart devices make our life connected to Internet all the time. But, the battery capacity is a major setback for Internet connected Smart phones. We know that TCP is the backbone protocol for these Internet data transmissions. In this paper, we studied the impacts of TCP connection flow on Smart phone battery. We have conducted extensive experiments and collected data from various operator networks in different countries. We observe that TCP delayed closures at the client and prolonged TCP zero Window probes (ZWP) from the server lead to increased signaling overhead and radio ON time. These phenomenon cause unwanted battery drain at the smart phones. To address these problems, we propose a solution that prevents unnecessary packet communication due to TCP delayed closures and ZWPs. Our solution has been evaluated for Android devices such as Samsung Galaxy S7 and S8 variants and Tizen device such as Samsung Z3. We have found that, our solution consistently achieves 10 to 20% of power usage reduction irrespective of varying network and traffic conditions.

Smart phones have made our life easier by providing 24×7 connectivity to Internet. But, the battery capacity is a major setback for Internet connected Smart phones. We know that TCP is the backbone protocol for these Internet data transmissions. In this paper, we studied the impacts of TCP flow control mechanism on battery life. Generally, TCP flow control mechanism is used to avoid buffer overflow at the receiver and is achieved by advertising the receive buffer size as TCP window to the sender. When sender receives zero TCP window, it stops sending data and starts sending probes to receiver till space opens up in the input window. During our study, we observed some abnormal behavior, whenever video streaming or downloads were interrupted. We found that there were prolonged TCP Zero Window Probes(ZWPs) from the sender which lead to increased signalling overhead and radio ON time at receiver. This phenomenon causes unwanted battery drain. To address this particular problem, we propose a solution that prevents unnecessary packet communication due to ZWPs. We have implemented our solution for Android devices and analyzed the performance extensively based on real time measurements. In our experiments, we could achieve 10-15% of power gain consistently in smart phones.