Cross-layer optimizations

"Cooperative communication can improve the performance of cellular mobile networks. The optimization techniques based on
duality theory, decomposition and subgradient method were applied for such wireless cellular cooperative systems. It was
shown that the joint optimization and resource allocation problem can be solved efficiently within a network utility maximization
framework. A concept of proportional fairness was used to achieve fair distribution of quality of service among users. Simulation
results confirm the validity of the theoretical work."

Wireless Sensor Networks (WSNs) are distributed systems composed of battery-powered nodes that sense and collect information about the physical world. They enable applications in a wide variety of domains including but not limited to environmental monitoring, health care and disaster management. In such applications, nodes communicate the
sensed information over multiple radio links until it reaches its destination referred to as the sink. As wireless communication is the most energy hungry operation, the data collection causes the biggest drain from battery. This motivates research on energy efficient mechanisms for data collection.

Though there is a plethora of protocols proposed in research literature, they are not designed to collaborate with the appli-cations. One opportunity from such collaboration is exploiting complete knowledge about application characteristics to make data collection more energy efficient. This enables underlying layers not to provision the resources more than the needs of the application and therefore save valuable battery power. The aim of this thesis is to explore a complex interplay between application characteristics and adaptive mechanisms across network stack using concrete real world deployments. It will propose a generic framework that integrates the adaptations to achieve near-optimal energy efficiency for heterogeneous applications.

Real-time asset tracking in indoor mass production manufacturing environments can reduce losses associated with pausing a production line to locate an asset. Complemented by monitored contextual information, e.g. machine power usage, it can provide smart information, such as which components have been machined by a worn or damaged tool. Although sensor based Internet of Things (IoT) positioning has been developed, there are still key challenges when benchmarked approaches concentrate on precision, using computationally expensive filtering and iterative statistical or heuristic algorithms, as a trade-off for timeliness and scalability. Precise but high-cost hardware systems and invasive infrastructures of wired devices also pose implementation issues in the Industrial IoT (IIoT). Wireless, self-powered sensors are integrated in this paper, using a novel, communication-economical RSSI/ToF ranging method in a proposed semantic IIoT architecture. Annotated data collection ensures accessibility, scalable knowledge discovery and flexibility to changes in consumer and business requirements. Deployed at a working indoor industrial facility the system demonstrated comparable RMS ranging accuracy (ToF 6m and RSSI 5.1m with 40m range) to existing systems tested in non-industrial environments and a 12.6-13.8m mean positioning accuracy.

This paper deals with measurements of the impact of fragmentation threshold tuning on speech quality and background traffic throughput in mixed voice/data transmission in an environment of WLANs (IEEE 802.11b). The ITU-T G.729AB encoding scheme is deployed in this study and the Distributed Internet Traffic Generator (D-ITG) is used for the purpose of the background traffic generation. The primary goal of generated background traffic is to affect the speech transmission by changing of VoIP connection network performance parameters such as jitter (delay variation), and packet loss. In general, those parameters have a significant impact on overall speech quality perceived by user. The speech quality and performance of background traffic are assessed by means of the accomplished PESQ algorithm and Wireshark network analyzer, respectively. This experiment shows that fragmentation threshold tuning can significantly decline speech quality and performance of background traffic.

Frequent changes in network topology due to mobility and limited battery power of the mobile devices are the key challenges in the adhoc networks. The depletion of power source may cause early unavailability of nodes and thus links in the network. The mobility of nodes causes frequent routes breaks and adversely affects the required performance of the applications. We propose a cross layer design for the dynamic
power control protocol and link prediction (DPCPLP) that provides a combined solution for power conservation as well as link availability. This combines the effect of optimum transmit power and received
signal strength based link availability estimation with AODV routing protocol using cross layer approach.
This method proposes to use optimum transmit power for transmitting the packets to a neighboring node to
increase the battery life of adhoc nodes and received signal strength based link prediction to increase the availability of the links. In this paper, the transmit power and received signal strength of the packets are cross-layer interaction parameters to provide the combined solution for power conservation and reliable route formation with increased availability of links and thus the routes amongst sources and destinations.
Further, this increases network and nodes’ lifetime and capacity. It improves throughput and packet delivery ratio by spatial reuse, prior prediction of link breaks and initiating the route repair. It also reduces end-to-end delay and power consumption by use of optimum transmit power. Through simulations, we have shown that our proposed protocol shows better performance.

Due to the increasing number of chronic disease patients, continuous health monitoring has become the top priority for health-care providers and has posed a major stimulus for the development of scalable and energy efficient mobile health systems. Collected data in such systems are highly critical and can be affected by wireless network conditions, which in return, motivates the need for a preprocessing stage that optimizes data delivery in an adaptive manner with respect to network dynamics. We present in this paper adaptive single and multiple modality data compression schemes based on deep learning approach, which consider acquired data characteristics and network dynamics for providing energy efficient data delivery. Results indicate that: 1) the proposed adaptive single modality compression scheme outperforms conventional compression methods by 13.24% and 43.75% reductions in distortion and processing time, respectively; 2) the proposed adaptive multiple modality compression further decreases the distortion by 3.71% and 72.37% when compared with the proposed single modality scheme and conventional methods through leveraging inter-modality correlations; and 3) adaptive multiple modality compression demonstrates its efficiency in terms of energy consumption, computational complexity, and responding to different network states. Hence, our approach is suitable for mobile health applications (mHealth), where the smart preprocessing of vital signs can enhance energy consumption, reduce storage, and cut down transmission delays to the mHealth cloud.

In this paper, we propose a novel position-based routing protocol designed to anticipate the characteristics of an urban VANET environment. The proposed algorithm utilizes the prediction of the node's position and navigation information to improve the efficiency of routing protocol in a vehicular network. In addition, we use the information about link layer quality in terms of SNIR and MAC frame error rate to further improve the efficiency of the proposed routing protocol. This in particular helps to decrease end-to-end delay. Finally, carry-n-forward mechanism is employed as a repair strategy in sparse networks. It is shown that use of this technique increases packet delivery ratio, but increases end-to-end delay as well and is not recommended for QoS constraint services. Our results suggest that compared with GPSR, our proposal demonstrates better performance in the urban environment.

The main focus of this article is to achieve prolonged network lifetime with overall energy efficiency in wireless sensor networks through controlled utilization of limited energy. Major percentage of energy in wireless sensor network is consumed during routing from source to destination, retransmission of data on packet loss. For improvement, cross layered algorithm is proposed for routing and retransmission scheme. Simulation and results shows that this approach can save the overall energy consumption.

Cross layer optimization plays a key role in radio resource management of broadband wireless systems (BWS).
Maximal SNR (MaxSNR) and Round Robin (RR) are two conventional scheduling strategies which emphasize
efficiency and fairness respectively. Proportional Fair (PF) provides a tradeoff between efficiency and fairness.
Here, we tailor PF to OFDM-based BWS (OPF). To meet QoS requirements for multi-rate services in multimedia
systems, we propose two algorithms: Adaptive OPF (AOPF) and Multimedia AOPF (MAOPF). Under time
varying and frequency selective fading wireless channel, system performances of OPF, AOPF, MAOPF are
evaluated and compared with conventional MaxSNR and RR. We define user satisfaction rate and average user
rate as optimization indication. Joint PHY and MAC simulation results show that OPF gives a good compromise
between system throughput and fairness by providing the highest user satisfaction rate; MAOPF favors the high
date rate by increasing throughput while providing the highest average user rate.

Wireless networks are poised to support a myriad of existing as well as emerging multimedia streaming applications. With increase in use of wireless local area networks (WLANs), ranging from simple data transfer to bandwidth-intense, delay-sensitive, and
loss-tolerant multimedia applications, issues regarding quality of service (QoS) have become a major research endeavor. Several challenges posed by robust video streaming over 802.11 WLANs include coping with packet losses caused by network buffer overflow, link erasures, or deadline misses. A major observation is
that the retry limit settings of the MAC layer can be optimized in a way such that the overall packet loss caused by link erasure, buffer overflow, or deadline miss is minimized. In this paper, we propose a novel solution for minimizing packet loss in multimedia applications over WLANs. Simulation results justify the improved quality of the received video based on our proposed approach.

Cross layer resource allocation in the wireless networks is approached traditionally either by communications networks or information theory. The major issue in networking is the allocation of limited resources from the users of network. In traditional layered network, the resource are allocated at medium access control (MAC) and the network layers uses the communication links in bit pipes for delivering the data at fixed rate with the occasional random errors. Hence, this paper presents the cross-layer resource allocation in wireless network based on the proposed social-sine cosine algorithm (SSCA). The proposed SSCA is designed by integrating social ski driver (SSD) and sine cosine algorithm (SCA). Also, for further refining the resource allocation scheme, the proposed SSCA uses the fitness based on energy and fairness in which max-min, hard-fairness, proportional fairness, mixed-bias and the maximum throughput is considered. Based on energy and fairness, the cross-layer optimization entity makes the decision on resource allocation to mitigate the sum rate of network. The performance of resource allocation based on proposed model is evaluated based on energy, throughput, and the fairness. The developed model achieves the maximal energy of 258213, maximal throughput of 3.703, and the maximal fairness of 0.868, respectively.

This paper deals with design of novel VoIP link adaptation algorithm from the speech quality critical conditions point of view. The algorithm design is based on cross-layer optimization principle. In the case of this algorithm, the cross-layer optimization strategy uses an interaction of application layer and network layer methods, such as a method of improved speech quality critical conditions detection based on PESQ score (speech quality parameter) and GTS function, respectively. The primary goal of this algorithm is improving speech quality in the VoIP connections, which are established in the respective link. The assessment of speech quality is carried out by means of the accomplished PESQ algorithm. The GTS function is used for the purpose of transmission bit rate control (limitation). Our experiments show that this approach can lead to the improvement of speech quality in respective VoIP connections.