Resource Allocation for Secure Communication Systems: Algorithmic Solvability

This paper presents a framework to minimize energy consumption in the medium access control (MAC) layer for wireless sensor networks. While satisfying a range of quality of service (QoS) requirements, such as the packet transmission success rate and maximum delay constraint, we optimally choose the lengths of periods in which sensors are active and inactive, such that the energy consumption per unit time in the entire network is minimized. We first use our framework to optimize the values of the MAC attributes macBeaconOrder and macSuperframeOrder in an IEEE 802.15.4 beacon-enabled star network. Then we consider a much simpler protocol, which we call "select-and-transmit" (S&T), and the same framework is applied to find the optimal lengths of the active and inactive portions. Finally, we compare the minimal energy consumption of the IEEE 802.15.4 MAC and S&T under the same QoS requirements and show that the IEEE 802.15.4 MAC outperforms S&T in most cases. However, the S&T MAC performs better than the standard under our framework in certain scenarios, e.g., event-driven sensor networks where the packet transmission success rate is usually low.

Wireless Body Area Network (WBAN) represents one of the most promising networks to provide health applications for improving the quality of life, such as ubiquitous e-Health services and real-time health monitoring. The resource allocation of an energy-constrained, heterogeneous WBAN is a critical issue that should consider both energy efficiency and Quality of Service (QoS) requirements with the dynamic link characteristics, especially when the limited resource cannot satisfy the expected QoS requirements. In this paper, a buffer aware Energy-efficient and QoS-effective resource allocation scheme is proposed in which the sensor queue buffer states, constraints of QoS metrics and the characteristics of dynamic links are considered. Specifically, a buffer aware sensor evaluation method is designed to dynamically evaluate the sensor state with considering the sensor buffer states for improving the system performance. We then formulate the resource allocation problem for optimizing the transmission power, the transmission rate and the allocated time slots for each sensor to minimize the sum mix-cost, which is defined to characterize the energy cost and QoS cost between attainable QoS support and QoS requirements. Simulation results demonstrate the effectiveness of the buffer aware sensor evaluation method and the proposed energy-efficient and QoS-effective resource allocation scheme.

The automobile industries are undergoing a technological revolution, which has facilitated the emergence of multiple Vehicle-to-Everything (V2X) applications with diverse Quality of Service (QoS) requirements. The conventional resource allocation methods are no longer applicable in V2X due to the diverse vehicular services with different QoS requirements in various practical scenarios. Therefore, this paper proposes a multi-service resource allocation strategy for V2X. Specifically, the problem of allocating resources for multiple V2X services is decoupled into two sub-problems, i.e., resource allocation between slices, and resource scheduling within slice. Then, a heuristic resource allocation algorithm is adopted to solve the resource allocation between slices sub-problem. For Real-Time (RT) and Non-Real Time (NRT) services in the vehicular network, specialized scheduling has been designed for the near-real-time Radio Access Network (RAN) Intelligent Controller and non-real-time RAN Intelligent Controller in the Open Radio Access Network (O-RAN) architecture, to meet the heterogeneous QoS service requirements of vehicles. Simulation results demonstrate that compared to the modified largest weighted delay first (M-LWDF) algorithm, the proposed resource allocation strategy can enhance the AoI of the proportional fairness (PF) services by 15%, and provide 7.39% transmission rate gain for NRT services.

Fuzzy logic based cross-layer design to improve Quality of Service in Mobile ad-hoc networks for Next-gen Cyber Physical System

Cross-layer based asymmetric resource allocation in relay-aided cognitive radio networks (CRN) is proposed in this paper. Existing cross-layer schemes on relay-aided CRN are based on symmetric resource allocation (RA) where the transmission duration is assumed to be symmetric between secondary base station (BS) to relay station (RS) and RS to mobile station (MS) links. This may not be realistic and hence our proposed model considers asymmetric resource allocation (ARA) in which the transmission durations are asymmetric. Very little work has been done in ARA for relay-aided networks and most of them considered only power allocation. The queue stability and quality of service (QOS) requirements are other considerations that have a significant impact on RA and hence are considered in this work. The QOS requirements can be brought in terms of the maximum delay threshold and the minimum throughput requirements. The interference threshold is evaluated from the QOS of the primary radio network. Based on the queue state information, channel state information and QOS, resources are allocated to the secondary radio network. Our proposed ARA differs from existing ARA schemes in terms of additionally considering the subcarrier pairing for resource allocation. This work evolves an optimal cross-layer based ARA considering the above mentioned factors into account. The proposed ARA includes subcarrier allocation, subcarrier pairing and power allocation. Simulation results show that the proposed ARA scheme increases the system throughput and serves the edge users satisfactorily.

End-to-end network slicing in unmanned aerial vehicle (UAV) networks has attracted significant attention for delivering differentiated services tailored to the quality of service (QoS) requirements of user terminals (UTs). In a sliced UAV network, the dual mobility of both UTs and UAVs, combined with the three-layer association of UT-UAV-network slice (NS), makes handoff a critical and challenging problem for maintaining the communication performance of UTs. This paper proposes a sequential handoff process where a central controller makes handoff decisions for each UT sequentially according to the UT queue. This process is modeled as a Markov decision process (MDP). In the MDP, the state space includes NS available resources, UTs' QoS requirements, and UT queue information. Additionally, the reward function accounts for UTs' QoS and handoff overhead. The double deep Q-network (DDQN) technique is then employed to design a handoff algorithm. This algorithm achieves the optimization goal of ensuring UTs' QoS requirements while avoiding frequent handoffs by maximizing the total reward of the MDP. Additionally, a state-dependent exploration technique with adaptive intensity adjustment, called noisy network (NoisyNet), is utilized to drive the DDQN for effective exploration, thereby enhancing the performance of the handoff algorithm. Finally, the runtime complexity of the proposed NoisyNet-DDQN-based sequential handoff (NoisyNet-DDQN-SH) algorithm is analyzed. The simulation results show that the NoisyNet-DDQN-SH algorithm converges well. Moreover, it exhibits strong robustness. Additionally, the NoisyNet technique effectively enhances the algorithm's performance, making the NoisyNet-DDQN-SH algorithm superior to the vanilla DDQN (with an ϵ-greedy strategy), greedy, maximum signal-to-interference-plus-noise ratio (MaxSINR), and random algorithms.

Wireless mobile radio systems have to serve users with different quality of service (QoS) requirements. Scheduling algorithms like weighted proportional fair (WPF) have been proposed considering channel gains to improve the system capacity in terms of sector throughput as well as user priorities to fulfill QoS requirements. These scheduling algorithms allow assigning different priority factors to users and services so that different QoS requirements can be fulfilled. This paper provides an analysis of the influence of users with different priorities on the sector and user throughput. It is shown analytically and by system level simulations that the average user throughput can be adjusted by choosing a specific priority factor. Furthermore, it is shown that the sector throughput decreases if WPF scheduling for users with different priorities and full buffer model are considered compared to a scenario with users having equal priority. If QoS requirements for realistic traffic models, e.g. FTP, have to be fulfilled, for each high priority user low priority users have to be removed from the system in order not to exceed an acceptable number of unsatisfied users. It is shown that the decrease in sector throughput with a realistic traffic model and QoS requirements is higher than for the full buffer model.

The integration of multiple Radio Access Technologies (RATs) of licensed or unlicensed bands is considered as a cost-efficient way to greatly increase network capacity of mobile networks. In this paper, we propose a Smart Aggregated RAT Access (SARA) strategy with aim to maximize network throughput while meeting diverse traffic Quality of Service (QoS) requirements. We consider a scenario where users with different QoS requirements access to the Heterogeneous Network (HetNet) with coexisting Cellular-WiFi. In order to maximize network resource utilization in such a complex and dynamic environment, we exploit multi-agent reinforcement learning to perform RAT selection in conjunction with resource allocation for individual users based on sensing dynamic channel states and traffic characteristics. We first use Nash Q-learning to provide a set of feasible RAT access strategies, and then employ Monte-Carlo (MCTS) based Q-learning to perform resource allocation which tries to maximize system throughput while meeting traffic QoS requirements. Numerical results reveal that the network access capacity can be maximized while meeting traffic QoS requirements with limited number of searches by using our proposed SARA. Compared with traditional WiFi offloading schemes, SARA can significantly improve system resource utilization and capacity while guaranteeing QoS requirements of UEs.

Wireless Body Area Networks (WBANs) deal with variety of healthcare services with diverse Quality of Service (QoS) requirements. However, QoS handling is a challenging problem in such networks. In general, QoS related problems can be addressed from different layers in the networking protocol suite. Design of an efficient QoS aware Medium Access Control (MAC) protocol can address this problem in MAC layer. This paper analyzes the QoS requirements of WBAN, identifies the requisites of QoS handling system, and outlines the trends that are being followed for its advancement with focus on QoS issues at MAC layer. We review some prior works, compare them, and analyze the current research concerned with problem of providing QoS in WBAN. We also explore some open issues and discuss them.

Component-based development (CBD) techniques have been widely used to enhance the productivity and reduce the cost for software systems development. However, applying CBD techniques to embedded software development faces additional challenges. For embedded systems, it is crucial to consider the quality of service (QoS) attributes, such as timeliness, memory limitations, output precision, battery constraints. Frequently, multiple components implementing the same functionality with different QoS properties can be used to compose a system. Also, software components may have parameters that can be configured to satisfy different QoS requirements. Composition analysis, which is used to determine the most suitable component selections and parameter settings to best satisfy the system QoS requirement, is very important in embedded software development process. In this paper, we present a model and the methodologies to facilitate composition analysis. We define QoS requirements as constraints and objectives. Composition analysis is performed based on the QoS properties and requirements to find solutions (component selections and parameter settings) that can optimize the QoS objectives while satisfying the QoS constraints. We use a multiobjective concept to model the composition analysis problem and use an evolutionary algorithm to determine the Pareto-optimal solutions efficiently.

Although cloud storage service (CSS) is convenient to access data, the cost, security, and performance are the issues to users who consider to adopt CSS. For example, CSS could be unavailable due to technical issues or natural disaster. In order to avoid single point of failure, many users duplicate data into multiple CSS providers (CSPs). However, this will increase cost; decrease security; and is difficult to maintain data consistency. In this paper, we propose a QoS-aware Distributed Cloud Storage Service (QDCSS) which aims to provide users with the appropriate CSS according to their quality of service (QoS) requirements. In QDCSS, the data will be encoded into code words by applying erasure code and then the code words will be smartly distributed to multiple CSPs to meet users' QoS requirements. Compared with data duplication, the numerical analysis show that QdCSS can reduce cost by 73% and improve security by 63%. Moreover, the simulation results show that QDCSS can adapt to different users' QoS requirements.

With the rapid development of BSG/6G and cellular vehicle-to-everything (C-V2X) technologies, the service demands of connected vehicles are diversifying, and the quality of ser-vice (QoS) requirements are also significantly improving. How to reasonably allocate the resources of C-V2X network and maximize the service quality has aroused wide concern. To this end, this paper proposes a dynamic base station (BS) accessing and resource allocation method for the downlink BSs of the next generation fully-decoupled C-V2X network, which allocates network resources according to the types of vehicles with different QoS requirements. Specifically, vehicles select the access BSs cluster for multi-connectivity, and BSs allocate appropriate band-width resources according to the QoS requirements. This paper proposes to use the Lyapunov stochastic optimization method to transform the long-term optimization problem, that is, to optimize the vehicle QoS and resource management dynamically during a period of time, into the optimization of the Lyapunov drift-plus-penalty function on each time slot, and use Markov approximation technique to approximate the selection of the global optimal access BSs cluster dynamically for either the delay-sensitive vehicles or the high-rate-guaranteed vehicles. By comparing the performance of this method with the benchmark approach, it is confirmed that the method proposed in this paper has certain advantages.

Massively Multiplayer Online Games (MMOG) are a class of computationally-intensive client-server applications with severe real-time Quality of Service (QoS) requirements, such as the number of updates per second each client needs to receive from the servers for a fluent and realistic experience. To guarantee the QoS requirements, game providers over-provision to game sessions a large amount of their resources, which is very inefficient and prohibits any but the largest providers from joining the market. In this paper, we present a new approach for cost-efficient hosting of MMOG sessions on Cloud resources, provisioned on-demand in the correct amount based on the current number of connected players. Simulation results on real MMOG traces demonstrate that compute Clouds can reduce the hosting costs by a factor between two and five. The resource allocation is driven by a load balancing algorithm that appropriately distributes the load such that the QoS requirements are fulfilled at all times. Experimental results on a fast-paced game demonstrator executed on resources owned by a specialised hosting company demonstrate that our algorithm is able to adjust the number of game servers and load distribution to the highly dynamic client load, while maintaining the QoS in 99.34% of the monitored events.

Dynamic slot assignment protocol for QoS support on TDMA-based mobile networks

IEEE 802.16 is a standardisation for a broadband wireless access in metropolitan area networks (MAN). Since the IEEE 802.16 standard defines the concrete quality of service (QoS) requirement, a scheduling scheme is necessary to achieve the QoS requirement. Many scheduling schemes are proposed with the purpose of throughput optimisation and fairness enhancement, however, few scheduling schemes support the delay requirement. In this study, the authors propose a new scheduling scheme reflecting the delay requirement. Specifically, the authors add the delay requirement term in the proportional fair scheduling scheme and the scheduling parameters are optimised with respect to the QoS requirement. Therefore the QoS requirement is achieved without the excessive resource consumption.